Method for determination of co-occurences of attributes

ABSTRACT

A method, system, computer program selecting attribute sets of characterizing attributes of an object, selecting an attribute set of attributes of interest, assigning a likelihood for each characterized attribute set that the attribute set occurs when the attribute set of interest occurs (each likelihood determined using Bayesian computable classifiers on a dataset of attributes for actual samples), comparing each assigned likelihood against likelihood thresholds, and reporting the assigned likelihoods of the characterizing attribute set based on the likelihood thresholds. Markers may be identified for diagnosis and prognosis. Characterizing attributes may be gene expression levels and the attribute of interest may be drug sensitivity level, drug dose (absolute concentration or dose relative to some standard dose), dose of drug which causes half-maximal cellular growth rate, or logarithm base 10 (dose) where dose is the dose which yields half-maximal total cell mass accumulating.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority from U.S. patent applicationSer. No. 60/291,928 filed May 21, 2001 by the same inventors under thesame title, and from U.S. patent application Ser. No. 60/291,931 filedMay 21, 2001 by the same inventors under the title Methods of GeneAnalysis and Treating Cancer. U.S. patent application Ser. Nos.60/291,928 and 60/291,931 are hereby incorporated herein by reference.

TECHNICAL FIELD

[0002] The invention relates to methods and apparatuses for determiningco-occurences of attributes in objects. It also relates to attributesincluding biological response.

BACKGROUND ART

[0003] The discovery of correlations among pairs or k-tuples ofvariables has applications in many areas of science, medicine, industryand commerce. For example, it is of great interest to physicians andpublic health professionals to know which lifestyle, dietary, andenvironmental factors correlate with each other and with particulardiseases in a database of patient histories. It is potentiallyprofitable for a trader in stocks or commodities to discover a set offinancial instruments whose prices covary over time. Sales staff in asupermarket chain or mail-order distributor would be interested inknowing that consumers who buy product A also tend to buy products B andQ and this can be discovered in a database of sales records.Computational molecular biologists and drug discovery researchers wouldlike to infer aspects of molecular structure from correlations betweendistant sequence elements in aligned sets of RNA or protein sequences.

[0004] One formulation of the general problem which encompasses manydiverse applications, and which facilitates understanding of theprinciples described herein is a matrix of discrete features in whichrows correspond to “objects” (such as diseases, individual patients,stock prices, consumers, or protein sequences) and the columnscorrespond to features, or attributes, or variables (such as drugsensitivity, gene expression, lifestyle factors, stocks, sales items, oramino acid residue positions).

[0005] Given the vast amount of data and the valuable nature of theinformation available from large datasets, one wants to use efficienttechniques to assist in the determination of correlations. For example,large-scale datasets exists of DNA microarray studies. These can be usedto determine correlations between gene expression patterns and drugtreatments. This approach is urgently needed for the treatment of manydiseases and other conditions, for example cancer which involves manydifferent tissues and varieties of tumor types. However, the applicationof the proper data analysis methods will be critical for the efficientuse of these large-scale data sets.

[0006] Biologists are generally acquainted with the idea of correlatingindividual genes with specific physiological functions, and with the useof linear correlation methods, such as Pearson's correlationcoefficient. Although the linear, single-gene approach has yieldedsignificant advances in biomedicine, the complex, nonlinear nature oftissue demands the use of more sophisticated methods.

[0007] It is desirable to provide efficient means by which to determinecorrelations between attributes of objects.

DISCLOSURE OF THE INVENTION

[0008] In a first aspect of the invention provides, a base method foridentifying one or more characterizing attributes for an object that arelikely to co-occur with one or more attributes of interest for theobject. The method comprises the steps of selecting one or moreattribute sets of one or more characterizing attributes of the object,selecting an attribute set of one or more attributes of interest for theobject, assigning a likelihood for each characterized attribute set thatthe attribute set occurs for the object when the attribute set ofinterest occurs for the object (each likelihood determined using one ormore Bayesian computable classifiers on a dataset of attributes for aplurality of actual samples of the object), comparing each assignedlikelihood against one or more likelihood thresholds, and reporting theassigned likelihoods of the characterizing attribute set based on thelikelihood thresholds.

[0009] In another aspect the invention provides, a method comprising thesteps of, selecting one characterizing attribute set of one or moreattributes for the object, selecting an attribute of interest for theobject, assigning a likelihood for the characterized attribute set thatthe attribute occurs for the object when the attribute of interestoccurs for the object (the assigned likelihood determined using aBayesian computable classifier on a dataset of attributes for aplurality of actual samples of the object), comparing the assignedlikelihood against a likelihood threshold, and reporting the assignedlikelihood of the characterizing attribute set based on the likelihoodthreshold.

[0010] In another aspect the invention provides, a method comprising thesteps of, selecting one or more attribute sets of one or morecharacterizing attributes of the object, selecting an attribute set ofone or more attributes of interest for the object, assigning alikelihood for each characterized attribute set that the attribute setoccurs for the object when the attribute set of interest occurs for theobject (each likelihood determined using one or more Bayesian computableclassifiers on a dataset of attributes for a plurality of actual samplesof the object), determining a likelihood significance for each assignedlikelihood using artificial samples, and ranking the assignedlikelihoods of the characterizing attribute set using the likelihoodsignificance.

[0011] In another aspect the invention provides, a method comprising thesteps of accessing one of the systems described below.

[0012] In another aspect the invention provides, a base system used toidentify one or more characterizing attributes for an object that arelikely to co-occur with one or more attributes of interest for theobject using a dataset of samples of attributes for the object. Thesystem comprises a computing platform, and a computer program on acomputer readable medium for use on the computer platform in associationwith the dataset. The computer program comprises instructions toidentify a characterizing attribute for an object that is likely toco-occur with an attribute of interest for the object, by carrying outthe steps of one of the base methods.

[0013] The methods may be used for drug discovery by identifyingcharacterizing attribute sets for interaction by the drug using thesteps one of the base methods for drug sensitive attributes of interestdrug, and performing screens for drugs where growth in cells havingdesirably ranked characterizing attribute sets is drug sensitive.

[0014] The methods may be used for identifying markers for diagnostickits used to determine if a treatment is appropriate for a patient, byidentifying a gene expression level set to be tested for in the patientby carrying out the steps of one of the base methods.

[0015] The methods may be used for identifyg markers for diagnosis of aliving system by identifying an attribute set to be tested for in theliving system using the steps of one of the base methods. The methodsmay also be used for identifying markers for prognosis of a livingsystem by identifying an attribute set to be tested for in the livingsystem using the steps of one of the base methods. The diagnosis orprognosis may be with respect to a disease or syndrome type of apatient. The methods may also be used for identifing markers fordetermining the appropriateness of a therapy or treatment of a livingsystem by identifying an attribute set to be tested for in the livingsystem using the steps of one of the base methods.

[0016] In the above methods the attributes of the attribute set mayinclude protein concentrations. The protein concentrations may includetissue protein concentrations. The protein concentrations may includeserum protein concentrations.

[0017] In the above methods the attributes of the attribute set mayinclude molecular markers. The molecular markers may include bloodmolecular markers. The molecular markers may include tissue molecularmarkers.

[0018] In the above methods the attributes of the attribute set mayinclude clinical observables. The clinical observables may includemicroscopic clinical observables. The clinical observables may includemacroscopic clinical observables.

[0019] The markers may be for diagnostic kits used in the diagnosis, fordiagnostic procedures used in the diagnosis, for prognostic kits used inthe prognosis, or for prognostic procedures used in the prognosis.

[0020] A likelihood threshold for each characterizing attribute set maybe determined using the same Bayesian classifiers as the assignedlikelihood on a dataset of attributes for a plurality of artificialsamples of the object. Similarly, a likelihood threshold for eachcharacterizing attribute set may be determined by computing thosecharacterizing attribute sets with an assigned likelihood above a givenpercentile of all assigned likelihoods for the relevant attribute set.

[0021] Artificial samples may be created by randomizing the actual geneexpression levels for the characterizing attributes. Artificial samplesmay be created by transposing the actual gene expression levels for eachcharacterizing attribute to another characterizing attribute.

[0022] The assigned likelihoods of the characterizing attribute sets maybe compared against a likelihood threshold determined by computing thosecharacterizing attribute sets with an assigned likelihood above a givenpercentile of all assigned likelihoods for the relevant attribute set ofinterest.

[0023] The characterizing attributes may be gene expression levels andthe attribute of interest may be drug sensitivity level, drug dose(absolute concentration or dose relative to some standard dose) along anincreasing or decreasing scale, dose of drug which causes half-maximalcellular growth rate, or −logarithm₁₀(dose) where dose is the dose whichyields half-maximal total cell mass accumulating under otherwisestandard conditions.

[0024] Drug sensitivity level may represent growth inhibiting indiseased cells, a lack of growth inhibiting in diseased cells, patienttoxicity in healthy cells. The attributes may be represented in adataset taken from the NCI60 dataset. The Bayesian classifier may beselected from a group consisting of linear discriminant analysis,quadratic discriminant analysis, and a uniform/gaussian analysis.

[0025] The characterizing attribute sets ranked following comparison ofthe likelihood and the likelihood threshold may be reported. The rankedcharacterizing attributes sets may be reported to one of a groupconsisting of a computer readable file stored on computer readablemedia, a printed report, and a computer network. The assignedlikelihoods may be ranked by assigned likelihood and subranked bylikelihood significance. The assigned likelihood may be compared againsta likelihood threshold, and the assigned likelihood of thecharacterizing attribute set may be reported based on the likelihoodthreshold and the ranking of the assigned likelihood.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] For a better understanding of the present invention and to showmore clearly how it may be carried into effect, reference will now bemade, by way of example, to the accompanying drawings that show thepreferred embodiment of the present invention and in which:

[0027]FIG. 1 is a first Venn diagram of statistically significantresults of analyses employed in the preferred embodiment of theinvention;

[0028]FIG. 2 is a second Venn diagram of statistically significantresults of analyses employed in the preferred embodiment of theinvention;

[0029]FIG. 3 is a plot of results from a 2D QDA analysis of a datasetaccording to the preferred embodiment of the invention;

[0030]FIG. 4 is a plot of results from a 2D LDA analysis of a datasetaccording to the preferred embodiment of the invention;

[0031]FIG. 5 is a plot of results from a 2D QDA analysis of a datasetaccording to the preferred embodiment of the invention;

[0032]FIG. 6 is a plot of results from a 2D UGDA analysis of a datasetaccording to the preferred embodiment of the invention;

[0033]FIG. 7 is a plot of results from a 1D LDA analysis of a datasetaccording to the preferred embodiment of the invention;

[0034]FIG. 8 is a plot of results from a 1D UGDA analysis of a datasetaccording to the preferred embodiment of the invention;

[0035]FIG. 9 is an example flow chart of a computer program according tothe preferred embodiment of the invention;

[0036]FIG. 10 is an example block diagram of a system according to thepreferred embodiment of the invention;

[0037]FIG. 11 is an example flow chart of a computer program accordingto an alternate embodiment of the invention;

[0038]FIG. 12 is an example block diagram of a system according to analternate embodiment of the invention;

[0039]FIG. 13 is an example flow chart of a computer program accordingto an alternate embodiment of the invention;

[0040]FIG. 14 is an example block diagram of a system according to analternate embodiment of the invention;

[0041]FIG. 15 is an example flow chart of a computer program accordingto an alternate embodiment of the invention; and

[0042]FIG. 16 is an example block diagram of a system according to analternate embodiment of the invention.

MODES FOR CARRYING OUT THE INVENTION

[0043] A number of alternative base methods, systems and devices willnow be referred described, along with alternative applications for thosemethods, systems and devices. It is understood that these base methods,systems and devices and their alternative applications are by way ofdescription of preferred embodiments and are not limiting to theprinciples described and the application of those principles.

[0044] As previously set out, a base method identifies one or morecharacterizing attributes for an object that are likely to co-occur withone or more attributes of interest for the object. The method comprisesthe steps of selecting one or more attribute sets of one or morecharacterizing attributes of the object, selecting an attribute set ofone or more attributes of interest for the object, assigning alikelihood for each characterized attribute set that the attribute setoccurs for the object when the attribute set of interest occurs for theobject (each likelihood determined using one or more Bayesian computableclassifiers on a dataset of attributes for a plurality of actual samplesof the object), comparing each assigned likelihood against one or morelikelihood thresholds, and reporting the assigned likelihoods of thecharacterizing attribute set based on the likelihood thresholds.

[0045] In an alternative base method, the method comprises the steps of,selecting one characterizing attribute set of one or more attributes forthe object, selecting an attribute of interest for the object, assigninga likelihood for the characterized attribute set that the attributeoccurs for the object when the attribute of interest occurs for theobject (the assigned likelihood determined using a Bayesian computableclassifier on a dataset of attributes for a plurality of actual samplesof the object), comparing the assigned likelihood against a likelihoodthreshold, and

[0046] Reporting the assigned likelihood of the characterizing attributeset based on the likelihood threshold.

[0047] In a further alternative base method, the method comprises thesteps of; selecting one or more attribute sets of one or morecharacterizing attributes of the object, selecting an attribute set ofone or more attributes of interest for the object, assigning alikelihood for each characterized attribute set that the attribute setoccurs for the object when the attribute set of interest occurs for theobject (each likelihood determined using one or more Bayesian computableclassifiers on a dataset of attributes for a plurality of actual samplesof the object), determining a likelihood significance for each assignedlikelihood using artificial samples, and ranking the assignedlikelihoods of the characterizing attribute set using the likelihoodsignificance.

[0048] In a further alternative base method, the method comprises thesteps of accessing one of the systems described below.

[0049] As previously set out a base system is used to identify one ormore characterizing attributes for an object that are likely to co-occurwith one or more attributes of interest for the object using a datasetof samples of attributes for the object. The system comprises acomputing platform, and a computer program on a computer readable mediumfor use on the computer platform in association with the dataset. Thecomputer program comprises instructions to identify a characterizingattribute for an object that is likely to co-occur with an attribute ofinterest for the object, by carrying out the steps of one of the basemethods.

[0050] The base methods can be used for drug discovery by identifyingcharacterizing attribute sets for interaction by the drug using thesteps one of the base methods for drug sensitive attributes of interestdrug, and performing screens for drugs where growth in cells havingdesirably ranked characterizing attribute sets is drug sensitive.

[0051] The base methods can be used for identifying markers fordiagnostic kits used to determine if a treatment is appropriate for apatient, by identifying a gene expression level set to be tested for inthe patient by carrying out the steps of one of the base methods.

[0052] In the base methods, a likelihood threshold for eachcharacterizing attribute set can be determined using the same Bayesianclassifiers as the assigned likelihood on a dataset of attributes for aplurality of artificial samples of the object. Similarly, a likelihoodthreshold for each characterizing attribute set can be determined bycomputing those characterizing attribute sets with an assignedlikelihood above a given percentile of all assigned likelihoods for therelevant attribute set.

[0053] Artificial samples can be created by randomizing the actual geneexpression levels for the characterizing attributes. Artificial samplescan be created by transposing the actual gene expression levels for eachcharacterizing attribute to another characterizing attribute.

[0054] The assigned likelihoods of the characterizing attribute sets maybe compared against a likelihood threshold determined by computing thosecharacterizing attribute sets with an assigned likelihood above a givenpercentile of all assigned likelihoods for the relevant attribute set ofinterest.

[0055] For the base methods, the characterizing attributes may be geneexpression levels and the attribute of interest may be drug sensitivitylevel, drug dose (absolute concentration or dose relative to somestandard dose) along an increasing or decreasing scale, dose of drugwhich causes half-maximal cellular growth rate, or -logarithm₁₀(dose)where dose is the dose which yields half-maximal total cell massaccumulating under otherwise standard conditions.

[0056] Drug sensitivity level may represent growth inhibiting indiseased cells, a lack of growth inhibiting in diseased cells, patienttoxicity in healthy cells. The attributes may be represented in adataset taken from the NCI60 dataset. The Bayesian classifier may beselected from a group consisting of linear discriminant analysis,quadratic discriminant analysis, and a uniform/gaussian analysis.

[0057] The characterizing attribute sets ranked following comparison ofthe likelihood and the likelihood threshold may be reported. The rankedcharacterizing attributes sets may be reported to one of a groupconsisting of a computer readable file stored on computer readablemedia, a printed report, and a computer network. The assignedlikelihoods may be ranked by assigned likelihood and subranked bylikelihood significance. The assigned likelihood may be compared againsta likelihood threshold, and the assigned likelihood of thecharacterizing attribute set may be reported based on the likelihoodthreshold and the ranking of the assigned likelihood.

[0058] The modes described herein provide extensions and alternatives tothe base methods described above and employ many similar principles. Theprinciples of one application as described herein may be applied to theothers as appropriate. Thus, the description of all elements of eachapplication will not always be repeated for all applications.

[0059] In the preferred embodiment it is preferred for simplicity ofprogramming and interpretation to consider the object and attributes inthe form of a matrix, see for example Table 1; however, this is notstrictly required and any of the embodiments can utilize a data set ofobjects and attributes that are not represented in the form of a matrixby sampling the data set directly. TABLE 1 Sample Object Attributes 1 AI d e f 2 B II d g h 3 A I d h

[0060] As an example of a dataset laid out in matrix format, the objectsmay be a particular disease, while the samples are taken from differentpatients and the attributes are particular expression levels ofparticular genes and sensitivity to a particular drug. The samples maybe cells. Using the data in Table 1, sample 1 from a cell having diseaseA is taken from a first patient. The disease A cell from the patient hassensitivity to drug I and gene expression levels d, e, f. Similarly,sample 2 from a cell having disease B may also be taken from the samepatient. The disease B cell from the patient has sensitivity to drug IIand gene expression levels d, g, h. Sample 3 from a cell having diseaseA is taken from a different patient. The disease A cell from the patienthas sensitivity to drug I and gene expression levels d, h.

[0061] For the example set out above, we may be interested in whether ornot sensitivity to drug I is related somehow to gene expressions levelsd and e together. Thus, drug I is an attribute set of interest and geneexpression levels d and e are a characterizing attribute set. This maybe represented in a matrix in the form of Table 2. TABLE 2Characterizing Attribute set Sample Object Attribute set of Interest I de 1 A yes yes 2 B no no 3 A yes no

[0062] Alternatively, object A and object B may be part of a genericobject C. For example, one may be interested in knowing if a number offorms of cancer are sensitive to the same drug. In this case, therelevant samples may change. In the example above, the first patient hastwo forms of cancer A and B. If one is looking for drug sensitivity inboth cancers A and B then the all the samples may be relevant, while theobject is cancers of type A and B. This permits the use of samples fromthe same patient for different cancers. Samples from the same patientwith the same attribute of interest would ordinarily be considered to beonly one sample. The particular definition of objects, samples,attributes of interest and characterizing attributes is a matter ofchoice for the designer of a particular embodiment. It is recognizedthat some choices may be superior to others; however, that does notbring them any of them outside of the principles described herein.

[0063] The datasets may contain many different samples, some of whichwill not contain attribute sets of interest for a given run of themethods. These can be filtered out before the methods are run, or theymay be left in the dataset to be accessed when the methods are run.

[0064] Each of the features for an object may be numerical orqualitative. The features are transformed into ordinal (values capableof being ordered) variables, termed attributes.

[0065] The principles described herein can be extended to attributessets of interest and characterizing sets of higher orders. For example,one may want to know if sensitivity to a particular cocktail of drugsco-occurs with a particular combination of gene expression levels.

[0066] In this description, specific reference is made on many occasionsto examples in the biotech industry. This is in no way limiting to thebroad nature of the principles described herein which may be applied tomany industry including, by way of example only, financial services,drug discovery, discovery and analysis of genetic networks, salesanalysis, direct mail and related marketing activities, clusteringcustomer data, analysis of medical, epidemiological and public healthdatabases, patient data, causes of failures and the analysis of complexsystems.

[0067] When using the phrases “occurs for” and “attributes for” inrespect of an object, it is understood that these are broadly intended.Attributes may not simply be a part of an object, such as its geneexpression levels, but may be factors or things that could broadly berelated to the object, such as weather on a particular day (attribute)may be related to the price (attribute) of an agricultural stock(object). It is also understood that objects are not limited totraditionally tangible objects, but may be intangible objects such asbonds or stocks as well.

[0068] It is recognized that a characterizing attribute set that islikely to co-occur with an attribute set of interest does notnecessarily imply that the characterizing attribute set is causing theattribute of interest; however, in many situations this informationcontinues to be useful. For example, symptoms (characterizingattributes) may act as a useful disease marker (attribute of interest);however, they are caused by, and do not generally cause, the disease.

[0069] The methods can form part of methods for identifying possibledrug targets. Once it is known that a disease or diseased cell isaffected by drugs that appear to interact with cells having particularcombinations of gene expression levels then screening studies can beconducted to find other drugs that also inhibit growth in cells withthose combinations of expression levels.

[0070] The base method takes a dataset of samples of objects, includinga characterizing attributes set and an attribute set of interest, asinput. The method generates an output display of characterizingattribute sets that have a substantial likelihood of co-occurring withthe attribute set of interest.

[0071] As part of the method, one or more characterizing attribute setsare selected, and one or more attribute sets of interest are selected.The likelihood of each characterizing attribute set co-occurring inactual samples of the object is determined using a Bayesian computableclassifier. A likelihood of each characterizing set occurring inartificial samples is used to determine a likelihood threshold. Onlythose characterizing attribute sets with a likelihood co-occurrencegreater than its likelihood threshold is selected.

[0072] For example, an embodiment of the method may take a collection ofbiological samples, their gene expression measurements (characterizingattributes), and a binary high/low drug response measurement (attributesof interest) as input. The method generates a prioritized list of genes,ranked by their p-values or ability to correctly predict the drugresponse (likelihood of co-occurrence). In this example, the methodconsists of three steps:

[0073] 1) Selection of candidate gene sets (characterizing attributeset).

[0074] 2) Calculation of classification accuracy for each gene set usinga Bayesian classifier (determination of likelihood of co-occurrenceusing Bayesian classifier)

[0075] 3) Ranking of the gene sets by their classification accuracy andthe identification of meaningful gene sets by a comparison of theirclassification accuracies with those generated using randomized data(determination of likelihood threshold using artificial samples andselection of characterizing attribute sets having a substantiallikelihood of co-occurrence).

[0076] Step 1) can take a number of forms. A simple list of all singlegenes can be a collection of (singleton) gene sets. A list of all pairsof genes can be a collection of (gene pair) candidate gene sets.Pre-processing techniques (such as those described in PCT PatentApplication PCT/CA98/00273 filed Mar. 23 1998 under title CoincidenceDetection Method, Products and Apparatus, inventor Evan W. Steeg,published Oct. 1 1998 as WO 98/43182) may be used to create candidategene sets. Alternative pre-processing techniques may be used, includingby way of example, standard feature detectors, or known gene pathwaytables.

[0077] Step 2) can also take a number of forms. Classical statisticaltechniques such as Linear Discriminant Analysis or QuadraticDiscriminant Analysis can be used. Other probabilistic models, such asthe Gaussian/Uniform, can be tailored to particular applications or tosuit biological intuition.

[0078] Step 3) involves the comparison of the classification scores fromstep 2) to those generated from randomized data Multiple datasets (onthe order of 100 or more) are generated by permuting the gene expressionvalues over the samples. i.e. if samples were rows and genes werecolumns in a table, we would permute the entries in each column,independently. Steps 1) and 2) are repeated for the randomized data, andthe scores from the real data are compared to the scores from therandomized data The scores are ranked according to those most likely toindicate a co-occurrence and those scores greater than the scores forrandomized data. Selections can be made according to the rank of thescores for the non-randomized data, or according to the rank of thedifference of the scores for the real and randomized data. Selectionsmay also be based on other calculations using the real and randomscores.

[0079] By way of example, validation can be determined either bycomparing classification scores from the real data to all theclassification scores from the randomized data and then applying theBonferroni correction, or by comparing the most extreme classificationaccuracies from each randomized trial to the most extreme classificationaccuracy from the real data An empirical p-value can be obtaineddirectly by calculating the proportion of random datasets for whichtheir extreme classification accuracies exceeded that in the real data.Only those gene sets with p-values below a user-selected cutoff arereported.

[0080] The results of the method described above have many usesincluding, by way of example, to use the:

[0081] 1) gene sets identified as potential targets for druginteraction.

[0082] 2) gene sets identified for pre-treatment screening of patientsto identify the most effective drug treatment.

[0083] We analyzed data on the responses of 60 human cancer cell lines(NCI60) to 90 drugs shown to inhibit their growth in culture(Developmental Therapeutics Program, National Cancer Institute). Thesedata were correlated with the basal (untreated) gene expression patternsfrom the same set of cell lines (see Ross, D. T., Scherf, U., Eisen, M.B., Perou, C. M., Rees, C., et al. (2000) Systematic variation in geneexpression patterns in human cancer cell lines. Nature 24, 227-235, andScherf, U., Ross, D. T., Waltham, W., Smith, L. H., Lee, J. K., et al.(2000) A gene expression database for the molecular pharmacology ofcancer. Nature 24, 236-244).

[0084] We compared linear and nonlinear methods for correlating geneexpression levels of individual genes with drug sensitivity for 1000genes across the 60 cancer cell lines, which included breast, centralnervous system, colon, lung, renal, and prostate cancer, as well asmelanoma and leukemia cell lines. In addition, we correlated theexpression patterns of pairs of genes with drug sensitivities todetermine whether more than one gene was required to predict drugsensitivity in some cases.

[0085] We found that linear and non-linear methods captured different,although to some extent overlapping, correlations, suggesting specificgenes as markers for particular drug treatments. We also found thatexpression levels of combinations of genes should be considered asindicators of effective drug treatments, as these combinations sometimescontain information not found in the expression patterns of individualgenes considered in isolation.

[0086] We conclude that nonlinear and combinatorial, as well as linear,single-gene methods are appropriate for the efficient extraction of geneexpression-drug sensitivity relationships in cancer cell lines.Computational methods such as these should be useful in cancer diagnosisand treatment.

[0087] First, we divided drug sensitivity into low- and high-sensitivityclasses (creating possible attributes of interest):

[0088] Drug sensitivities were reported as −logGI50 s, with the logbeing base 10. All the drug sensitivities were normalized to mean zeroso that the measurement really reflected differential growth inhibition.We wanted to categorize the cell line response into “uninhibited” and“inhibited”, with a small gray area to avoid the effects of harshcutoffs. In that scale, a value of 1.0 for a cell line/drug combinationmeant that the cell line was inhibited to 50% growth at {fraction(1/10)} the dosage of the “average” drug. For our purposes, we wanted toidentify those drugs that were effective at least ⅕ the “average”dosage, which in the log scale turns into 0.7. Thus, any value of−logGI50 less than 0.7 were considered “uninhibited” or a lowsensitivity/response. On the other end of the scale, all of those drugsthat resulted in inhibition at concentrations<{fraction (1/10)} of theaverage dosage were all considered “inhibitory”. We then put in a smoothlinear scaling between the cutoffs of 0.7 (low response) and 1.0 (highresponse). This gave us the function:

f(r)=0 if r<0.7

(r−0.7)/0.3 if r in [0.7, 1)

1 if r>=1

[0089] Sensitivities in the range [0.7,1] are partially in both classes.Since it varies between 0 and 1, the function f can be viewed as a fuzzyclassification or a probability. f(r) Probability of sensitivity in highclass, 1−f(r)=Probability of sensitivity in low class.

[0090] Finding correlations (determining likelihood of co-occurrence ofattribute set of interest and characterizing attribute set) between drugsensitivity (attribute set of interest) and gene expression(characterizing attribute set):

[0091] For a given gene, A, and drug, B, we try to see if 2 classes ofcell lines (high and low sensitivity) can be distinguished on the basisof gene expression. One of the methods for finding correlations was aslightly modified version of LDA (slightly modified to account forpartial class membership). LDA consists of the following steps:

[0092] Fit a gaussian Gh to the gene expressions in the high sensitivityclass Ch and a gaussian Gh to gene expressions in the low sensitivityclass Cl, where |Ch| is the number of cell lines in the high sensitivityclass, and |Cl| is the number of cell lines in the low sensitivityclass.

[0093] Let Lexpr=expression of gene A in cell line L,Lsensitivity=sensitivity of cell line L to drug B

[0094] The mean of G1 is calculated as

sum from cell line L=1 to |Ch| of (Lsensitivity*Lexpr)/(sum ofsensitivities in Ch)

[0095] Mean and variance of G1 were calculated in a similar way.

[0096] Pooled variance of Gh and Gl was calculated

avg. variance=(Ch variance*sum Ch sensitivities+Cl variance*sum Clsensitivities)/(num cell lines−2−1)

[0097] We calculated the probability of a cell line, L, having highsensitivity as follows

P(L in Ch|Lexpr)=Gh(Lexpr)*P(Ch)/(Gh(Lexpr)*P(Ch)+(Gl(Lexpr)*P(Cl))

[0098] above is Equation 1

[0099] The error for this probability was calculated as

e=Lsensitivity−P(L in Ch|Lexpr).

[0100] Testing predictions:

[0101] For a given gene and drug we used cross-validation to testprediction of sensitivity from gene expression. Using 59 cell lines wedetermined gaussians Gh and G1 for the two sensitivity classes. Wepredicted the sensitivity class of the 60th cell line L, from its geneexpression, using the Equation 1 above. We repeated this procedure forall of the 60 cell lines and calculated a mean squared error for all ofthe predictions. e=sum L=1 to 60 [P(L in Ch|Lexpr)−Lsensitivity]{circumflex over ( )}2/60.

[0102] Searching for all correlations:

[0103] We applied the above method to all pairs of genes and drugs [1000genes]×[90 drugs]

[0104] Using other methods:

[0105] 1D discriminants

[0106] we also used 2 other methods similar to LDA, to search forcorrelations between sensitivity and gene expression

[0107] QDA—differs from LDA in that the original variances of Gh and Glare used in Equation 1, instead of the average of the variances as aresult, QDA can have nonlinear decision boundaries between classes whileLDA has linear decision boundaries.

[0108] uniform/gaussian discriminant—similar to LDA except uses uniformdistribution for the low class instead of a gaussian distribution, theassumption behind these distributions is that a specific mechanism isresponsible for high sensitivity (the gaussian distribution), whilevarious mechanisms lead to low sensitivity (uniform distribution), theheight of the uniform is calculated as 1/(max(expr)−min(expr))

[0109] 2D discriminants

[0110] The three methods above were extended to look for correlationsbetween pairs of genes and drug sensitivities. For a given pair ofgenes, the joint distribution of gene expression values was representedby gaussians and uniform distributions. A search for correlations wasconducted over all pairs of genes and all drugs. For each drug, thethree methods were applied to about ½ million (gene,gene,drug) triples.

[0111] Calculating statistical significance (a likelihood threshold):

[0112] The statistical significance of MSE scores was determined bycomparing against results from randomized data. Statistical significancewas adjusted by the Bonferroni method to account for multiple tests.(i.e. for a given drug the statistical significance of a score from a 1Ddiscriminant was multiplied by 1000; statistical significance of scoresfrom 2D discriminants was multiplied by 10{circumflex over (+5)}).

[0113] To determine whether linear and nonlinear methods could capturedifferent sets of gene expression-drug sensitivity correlations, weemployed linear discriminant analysis (LDA) and two nonlinear methods,quadratic discriminant analysis (QDA) and a Bayesian model (auniform/Gaussian discriminant). Results are shown in Table 3 below.TABLE 3 Drugs Drugs Genes Genes P <= 0.01 P <= 0.1 P <= 0.01 P <= 0.1LDA-1D 8 (40%) 29 (53%) 14 (24%) 43 (18%) QDA-1D 4 (20%) 24 (44%) 5 (8%)29 (12%) Bayes 5 (25%) 25 (45%) 6 (10%) 34 (14%) mixture 1D All 1 D 13(65%) 43 (78%) 20 (34%) 73 (31%) methods LDA-2D 9 (45%) 20 (36%) 24(41%) 102 (43%) QDA-2D 7 (35%) 22 (40%) 18 (30%) 84 (35%) Bayes 4 (20%)22 (40%) 9 (15%) 90 (38%) mixture 2D All 2D 16 (80%) 41 (74%) 48 (81%)218 (91%) methods Intersection 0 (0%) 4 (7%) 0 (0%) 1 (0.4%) of allmethods Union of all 20 (100%) 55 (100%) 59 (100%) 239 (100%) methods

[0114] Table 3 summarizes linear, nonlinear, 1D, and 2D analyses for1000 genes, 90 drugs, and 60 cell lines. Shown are the numbers ofstatistically significant gene-drug associations found at p<=0.01 and p21 =0.1. For example, the LDA-1D analysis method found that for each of8 drugs, at least one gene out of a group of 14 was able to predict highsensitivity at p<=0.01. For LDA-2D, 24 genes arranged in pairs were ableto predict high sensitivity to each of 9 drugs at p<=0.01.

[0115] All three methods identified statistically significantcorrelations between the expression levels of specific genes andsensitivity to drugs based on GI50 values (drug concentration thatinhibits cell growth by 50%). Although there was some overlap betweenthe findings of the different methods, they were generally complementaryto one another, as shown by the Venn diagrams of statisticallysignificant results from all analysis methods in FIGS. 1 and 2. A degreeof overlap occurs between results obtained; however, some of thegene-drug correlations were identified by a single method. As shown inFIG. 1, twenty-six drugs (represented by intersection 1) of the 29 drugs(represented by circle 3) found to be in significant correlations withgenes by linear 1D methods (LDA 1D) were also identified by at least oneother method in the non-linear and combinatorial methods that identified52 drugs (represented by circle 5), leaving 3 drugs (represented by thenon-intersecting portion 7 of circle 3) that were identified by LDA 1Dalone. Similarly, as shown in FIG. 2, five genes (non-intersectingportion 9) out of 43 (circle 11) that were identified by LDA ID asmarkers for drug sensitivity were identified by that method alone, whilethe remaining 38 genes (intersection 13) were identified by at least oneof the other methods in addition to LDA 1D out of a total of 234 genes(circle 15) that were identified by the other methods.

[0116] Nonlinear methods therefore identify gene-drug associations notfound by a linear method. This is the case for both 1-dimensional (1D)analysis involving correlations between a single gene and one drug, andfor 2D analysis involving correlations between pairs of genes and onedrug (gene, gene, drug triples).

[0117] To discover correlations between gene expression levels and drugsensitivities that involve more than a single gene, (i.e., theinformation that predicts high sensitivity to a drug may be contained inthe combination of expression patterns of two genes), we applied 2Ddiscriminants. This involved using the same three methods describedabove for single genes, except that in this case we searched forsignificant correlations between pairs of genes and individual drugs,i.e., gene, gene, drug triples. Results for 2D methods are shown inTable 3 and FIGS. 1 and 2. The 2D methods discovered correlations thatwere not identified by the 1D method. It is evident from FIGS. 1 and 2and Table 3 that relying only on single-gene (1D) correlations wouldhave missed a large proportion of the gene-drug associations, sincethese required the information contained in pairs of genes; this was thecase for all three correlation measures. Overall, the use of ourcombination of linear, nonlinear, 1D and 2D methods allowed for thediscovery of 239 marker genes for high drug sensitivity, while solereliance on the linear 1D method, LDA 1D, would have yielded only 43markers, or fewer than 20% of the total. Each of the six methodsidentified gene-drug correlations not found by any of the other fivemethods. LDA 1D yielded only five gene markers not identified by atleast one of the other methods. For QDA 1D, 1 gene was found by thismethod only. Uniform/gaussian 1D was the most effective of the 1Dmethods in this respect, yielding 9 genes correlated with highsensitivity found by this method only. By contrast, genes peculiar toeach 2D method included (in pair combinations) 52 genes for LDA, 32genes for QDA, and 49 genes for uniform/Gaussian.

[0118] An example of the 2D approach is diagrammed in FIG. 3. Expressionlevels of the gene elongation factor TU are plotted vs. expressionlevels of the gene SID W 116819 for the 60 cell lines, whosesensitivities to fluorodopan varied. The areas mapped out by theGaussian distributions separate most of the black (filled-in squares)points (highly sensitive) cell lines from the white (open squares)points Now sensitivity) cell lines, placing them in separate regions ofthe graph. Twelve cell lines with high sensitivity to fluorodopan (blackpoints) had varying levels of expression for both genes 1 and 2. In FIG.3, for either SID W 116819 or elongation factor TU alone, below zero (−)expression occurs in both high and low sensitivity cell lines;similarly, above zero (+) expression for each gene alone occurs in bothhigh and low sensitivity cell lines. Therefore, neither gene alonecorrelates with sensitivity. However, the genes can be used incombination to obtain a correlation between gene expression and highdrug sensitivity. Cell lines that are highly sensitive to fluorodopan(black points) tend to have greater than zero expression values for bothgenes (++), or below zero expression values for both genes (−−), whilethe combinations (+−) and (−+) tend to occur in cell lines that have lowsensitivity to fluorodopan (white points).

[0119] (The use of + and − here is an oversimplification to describe thegeneral distribution of black and white points on the graph in FIG. 3.)

[0120]FIGS. 3 through 6 depict 2D analysis of gene expression-drugsensitivity data for 60 cancer cell lines. FIG. 3 employs QDA analysis.Each point represents a cell line, with its location specified by therelative expression of two genes (x and y coordinates). The points arecoloured by the cell line's response to Fluorodopan. The contoursrepresent points of equal probability as predicted by the methodsdescribed herein. In general the areas where black squares tend to beconcentrated are areas of predicted high sensitivity. The arrowsindicate the direction of predicted increasing sensitivity. Theoutermost contour to the bottom left and top right show the decisionsurface generated by the two Gaussian distributions: outside theoutermost contour are classified as high response and the between thegradients as low response. Expression levels of SID W 116819 alone areuncorrelated with sensitivity because a plus (+) can correspond toeither high or low sensitivity, and a minus (−) can correspond to eitherhigh or low sensitivity; the same is true of elongation factor TU.However, as shown in Table 4 below, when either (+) or (−) co-occurs inboth genes, sensitivity is high. When expression levels of SID W116819and elongation factor TU have opposite signs, sensitivity is low.We therefore obtain a rule for the correlation of the pair of genes withfluorodopan sensitivity. TABLE 4 elongation SID W 116819 factor TUSensitivity + + High − − High − + Low + − Low

[0121] Other examples for the 2D methods are shown in FIGS. 4, 5 and 6,and their respective Tables 5, 6 and 7 below.

[0122] Referring to FIG. 4, according to LDA 2D method, both SID W242844 and SUD W 26677 are needed to predict high sensitivity tomitozolamide. For SID W 242844alone, (+) is associated with lowsensitivity only, while (−) can be associated with low or highsensitivity. For SID W 26677, (−) is always associated with low, and (+)can correspond to either high or low sensitivity. However, thecombination (−+) corresponds to high sensitivity only, so both genes areneeded to establish a correlation with high sensitivity TABLE 5 SID W242844 SID W 26677 Sensitivity + + Low − − Low − + High + − Low

[0123] Referring to FIG. 5, according to QDA 2D method, both SID W242844 and ZFP36 are needed to predict high sensitivity to mitozolamide.For SID W 242844, (−) can correspond to either high or low sensitivity,and (+) corresponds to low sensitivity. For ZFP36, (−) corresponds toeither high or low, and (+) corresponds only to low sensitivity.However, the combination (−−) corresponds only to high sensitivity, soboth genes are needed for the correlation. TABLE 6 SID W 242844 ZFP36Sensitivity + + Low − − High − + Low + − Low

[0124] Referring to FIG. 6, according to uniform/gaussian 2D, for thehigh sensitivity cell lines, expression of SID W 242844 tends to benegative (−), while expression of ESTs Chr.1 488132 tends to be positive(+). Both SID W 242844and human nucleotide binding protein are needed topredict high sensitivity to mitozolamide. For SID W 242844, (+) isalways associated with low sensitivity, and (−) can be associated witheither high or low. For ESTs Chr.1 488132, (−) is associated only withlow, and (+) can correspond to either high or low. The combination (−+),however, is associated with high, while all other combinations predictlow sensitivity. Therefore, both genes are needed to predict highsensitivity. TABLE 7 SID W 242844 ESTs Chr.1 488132 Sensitivity + + Low− − Low − + High + − Low

[0125] Many of the results could not be classified easily as simpleplus/minus distributions, but the concept of requiring a particularrange of expression value combinations for each pair of genes applies inall cases shown for the 2D methods. In some cases, this range of valuesincludes zero (no deviation in expression from mixed culture control).This is acceptable, since we are interested only in relative basal geneexpression levels, not perturbed gene expression relative to thecontrol. For example, a combination of approximately zero (0) expressionfor gene SID 289361 and positive (+) expression for gene SID 327435correlated with high sensitivity to fluorouracil according to QDA 2D, inone case.

[0126] The 1D approach is shown in FIGS. 7 and 8. For single genecorrelations, only the value on the x-axis (horizontal axis) isconsidered. A random variable was used to create a y-axis(vertical-axis) as a visual aid to avoid the problem of overlappingpoints. Referring to FIG. 7, according to LDA 1D, cell lines with highsensitivity to mitozolamide exhibited high levels of PTN expression.Referring to FIG. 8, Uniform/gaussian ID determined that cells with highsensitivity to mitozolamide expressed DOC-2 mitogen-responsivephosphoprotein in a particular range of values above control. Randomvariable on y-axis permits visualization of data points that wouldobscure one another in a one-dimensional graph.

[0127] In some instances, we found significant correlations between agene and more than one drug. Generally, the drugs that correlated with agene were from the same class, however, this was not always the case.Results are shown in previously set out Table 3.

[0128] We determined that certain levels of expression for specificgenes are consistently associated with high sensitivity to drugs forcancer in 60 human cancer cell lines. Linear analysis methods alone wereinsufficient to identify many statistically significant correlationsbetween basal gene expression and high sensitivity to drugs. Inaddition, we have demonstrated the need for 2D methods, as in manycases, combinations of genes contain the information required toestablish correlations with drug sensitivity. This suggests that thephysiological functions of cancer cells are often governed by thesynergistic actions of multiple genes. These results are consistent withthe idea that physiological systems are by nature complex, nonlinearsystems, and should be analysed as such.

[0129] As shown in Table 3 (where Bayes mixture refers to theUniform/Gaussian), every one of the six example methods, LDA, QDA, andUniform/Gaussian each for 1D and 2D analyses, identified gene-drugcorrelations not discovered by any of the other five methods. This isespecially true for the 2D methods. A combination of correlationtechniques is appropriate for efficient interpretation of DNA microarraydata.

[0130] The variability of cancer cell types poses two interrelatedproblems: 1) diagnosis, and 2) choice of treatment. Evidence has beenfound that the gene expression patterns of breast-derived cancer celllines reflect those of the normal tissue of origin and of abreast-derived tumor, suggesting that cell lines may be useful indetermining the gene expression patterns of in vivo cancer cells. Ifthis is the case, it should be possible to use the results oflarge-scale studies of gene expression and drug responses in cancer celllines to create databases of diagnostic markers for various cancers.Linear, nonlinear, and combinatorial analyses could be applied todetermine those markers, and to suggest appropriate therapeutic drugs.As we have demonstrated in the present study, the use of nonlinear andcombinatorial analyses in addition to linear, single-gene methods,increases the number of gene-drug associations, and therefore shouldimprove the probability of determining appropriate drug therapies.

[0131] Markers identified by these computational methods could be usedas the basis for diagnostic tests specific for those genes, perhaps inthe form of smaller-scale microarray assays. Tests such as these wouldbe aimed directly toward determination of the best choice(s) fortherapeutic drug treatment. For example, a diagnostic test indicatinghigh expression levels for both genes elongation factor TU and SID W116819 (FIG. 3) would suggest a high probability of a response tofluorodopan treatment.

[0132] The present study focused on basal gene expression patterns asindicators of drug sensitivity.

[0133] In carrying out the embodiment described above for the NCI60dataset, we computationally distinguish strong from weak biologicalresponses (i.e., to discriminate, classify, or predict biologicalresponses). In its details, the method employs computationally-derivedassociations between computationally-analyzed quantitative geneexpression data and computationally-analyzed quantitative intensitydata. The intensity data represents observables (other than geneexpression) assumed to be related in some arbitrary, but graded, mannerto the biological responses.

[0134] We used a “biological response scoring function,” called f, wheref:U→R¹⊂[0,1], and U is a 1-parameter continuous path in R^(m), m >1. fis constructed to represent biological response on a bounded ordinalscale of real numbers, where

[0135] f=0 is interpreted to mean “no or negligible biologicalresponse”;

[0136] f=1 is interpreted to mean “very substantial, strong, or highbiological response”;

[0137] 0<f<1 is interpreted to mean “biological response somewherebetween negligible and substantial in proportion to proximity to 0 or 1,respectively.” Formally, the domain U of f is defined to be a1-parameter continuous path in m-dimensional space. E.g., U can simplybe scalar, i.e., U⊂R¹; or U can be an arbitrary 1-parameter path throughhigher-dimensional space R^(m), m>1 (e.g., a series of m-dimensionalfeature vectors indexed by continuous time). Note: The examples providedhere concentrate on the scalar domain case ( i.e., U⊂R¹), but theapproach also applies to cases of higher-dimensional continuous 1-parameter paths.

[0138] Domain U⊂R¹ is interpreted to mean:

[0139] “degree or intensity of external effect on the biology” either onan increasing or decreasing scale.

EXAMPLES

[0140] U represents drug dose (absolute concentration or dose relativeto some standard dose) along an increasing, or decreasing, scale;

[0141] U can represent the dose of drug which causes half-maximalcellular growth rate as charted along a scale which decreases to theright;

[0142] U represents -logarithm₁₀(dose), where dose is the dose whichyields half-maximal total cell mass accumulating in a chemostat underotherwise standard conditions (e.g., let r⊂U such that r=−logGI50=−logarithm₁₀(GI50), where GI50=drug dose which yields 50% of thecellular mass which is achieved under some standard untreated-with-drugconditions.

[0143] Note that in this last example, r increases as GI50 decreases. Inthis case, an increasing r represents a decreasing “intensity of doseneeded to obtain some defined biological effect.”

[0144] The function f assigns a readily interpretable numerical“biological response score” in the continuous interval [0,1] to a“degree or intensity of external effect on biology” from a scale U⊂R¹.Thus, f is what inexorably links “intensity of external effect onbiology” to a readily interpreted biological response scale, where theinterpretations of f values are given in 1a) above.

Example (Continuous Piece-Wise Linear Biological Scoring Function)

[0145] ${{Let}\quad {f(r)}} = \{ \begin{matrix}{0,{r < 0.7}} \\{{( {r - 0.7} )/0.3},{r \in \lbrack {0.7,1} )},{{{where}\quad r} = {{{- \log}\quad {GI50}} = {- {{{logarithm}_{10}({GI50})}.}}}}} \\{1,{r \geq 1}}\end{matrix} $

[0146] Interpretations:

[0147] If the dose required to achieve some biological effect (say, 50%growth inhibition) is small, then score this phenomenon as “strongbiological response”, i.e., “cells are very sensitive.” In f (r) terms,if GI50≦0.1 (i.e., −log(GI50)≧1), then f=1.

[0148] If the dose required to achieve some biological effect (say, 50%growth inhibition) is large, then score this phenomenon as “weakbiological response”, i.e., “cells are very insensitive.” In f (r)terms, if GI50≧0.2 (i.e., −log(GI50)≦0.7), then f=0.

[0149] If the dose required to achieve some biological effect (say, 50%growth inhibition) is modest or a some gradation between low and high,then score this phenomenon as “mixed-strength biological response”,i.e., “cells are somewhat sensitive and/or somewhat insensitive.” In f(r) terms, if 0.2≧GI50>0.1 (i.e., 0.7≦−log(GI50)<1), then f=(r−0.7)/0.3.

Example (Smooth Biological Scoring Function)

[0150] $\begin{matrix}{{{{Let}\quad {f_{sigmoid}(r)}} = {1 - ( {1 + ( \frac{r - a}{b - a} )^{v}} )^{- 1}}},{r \geq a},{b > a \geq 0},{v > 1},} \\{{{f_{sigmoid}(r)} = {1 - ( {1 + ( \frac{a - r}{b - a} )^{v}} )^{- 1}}},{r < a},{b > a \geq 0},{v > 1}}\end{matrix}$

[0151] where r=−log GI50=−logarithm₁₀(GI50)

[0152] Let:

[0153] i denote, or label, any given external effect, or situation, onthe biology, e.g., temperature, pH, therapeutic intervention, compoundapplied, drug dosed, etc. (For explanatory convenience, for now on weoften refer to any external effect on the biology as “drug.”)

[0154] j denote any biological source of gene expression data, e.g.,patient, tissue, cultured cell line, etc. (For explanatory convenience,for now on we often refer to any biological source of expression data as“cell line.”)

[0155] k denote, or label, any given gene, mRNA species, gene product,or protein. (For explanatory convenience, for now on we often refer toany of these entities as “gene.”)

[0156] g_(k) ^(l) denote, or label, gene abundance or expression level,however numerically adjusted or normalized, of gene k in cell line j.

[0157] a represent, or label, any desired categorical description ofbiological response score. E.g., a=any of “high”, “strong”,“sensitive/insensitive”, etc. if f=1; e.g. a=any of “low”, “weak”,“insensitive”, etc., if f=0; e.g., a =any of “middle”, “modest”, “mixedsensitive/insensitive”, etc. if 0<f<1.

[0158] w represent, or label, generally the biological response score(i.e., f value) of any biological source under any external effect orsituation, e.g., the sensitivity of a cell line to a drug.

[0159] w^(i,j) specifically denote, or label, the biological responsescore (i.e., f value) of biological source j under any external effector situation i, e.g., f value of cell line j under some specifiedexposure to drug i.

[0160] w_(a) ^(i,j) specifically denote, or label, the biologicalresponse score (i.e., f value) which falls in some particular category a(e.g., a=sensitive) of biological source j under any external effect orsituation i, e.g., w_(sensitive) ^(i,j) means the f value is 1 for cellline j under some specified exposure to drug i.

[0161] C_(a) ^(i) denote the set of biological sources falling inbiological response category a when the biological source is externaleffect i. E.g., C_(sensitive) ^(i) is the set comprising cell lines forwhich the respective f values are 1 when exposed to drug i at somespecified dose, i.e., the set of cell lines sensitive to drug i.

[0162] |C_(a) ^(i)| denote the cardinality of C_(a) ^(i), i.e., thenumber of elements in set C_(a) ^(i). E.g.,

[0163] |C_(sensitive) ^(i)|=23, means that for the collection of celllines considered, there are 23 cell lines that are sensitive to drug i.

[0164] For any given external biological effect i (e.g., drug iadministered by some specified dosing regime), and for any gene k, . . .

[0165] Compute a category-wise data-summarizing mathematical,statistical, machine learning-based, data mining-based, or empirical,etc. entities. For example:

[0166] Compute histogram comprising g_(k) ^(i), for given k, for jεC_(a)^(i). E.g., histogram of abundances of gene k from all the cell linessensitive to drug i.

[0167] Compute parameters necessary to fit any chosen mathematicaldensity function or continuous curve to a a category-wise histogram ofthe type described in 3a.1 above. E.g., in preparation for fitting agaussian distribution to {g_(k) ^(j)}, jεC_(sensitive) ^(i), computeparameters that are the cell line sensitivity-weighted gene k samplemean _(i){overscore (g)}_(k) ^(sensitive) and variance s² _(i)g_(k)^(sensitive), where $\begin{matrix}{{{{}_{}^{}{g\_}_{}^{}} = {w^{i,j}{g_{k}^{j}/{\sum\limits_{j}\quad w^{i,j}}}}},{j \in C_{sensitive}^{i}}} \\{{{{\, s^{2}}_{i}{\overset{\_}{g}}_{k}^{sensitive}} = {{w^{i,j}( {g_{k}^{j} - {{}_{}^{}{g\_}_{}^{}}} )}^{2}/{\sum\limits_{j}\quad w^{i,j}}}},{j \in C_{sensitive}^{i}}}\end{matrix}$

[0168] Compute a category-wise average data-summarizing parameters.E.g., sensitive\insensitive average variance are, respectively,$\begin{matrix}{{\langle{s^{2}{{}_{}^{}{}_{}^{}}}\rangle} = {( {{s^{2}{{}_{}^{}{}_{}^{}}{\sum\limits_{j^{\prime}}\quad w^{i,j^{\prime}}}} + {s^{2}{{}_{}^{}{}_{}^{}}{\sum\limits_{\hat{j}}\quad w^{i,\hat{j}}}}} )/}} \\{{( {{\sum\limits_{j^{\prime}}\quad w^{i,j^{\prime}}} + {\sum\limits_{\hat{j}}\quad w^{i,\hat{j}}}} ),}}\end{matrix}$

[0169] where j′εC_(sensitive) ^(i) and ĵεC_(sensitive) ^(i)

[0170] σ_(k) ^(avg)=the square root of the average variance.

[0171] For all a categories of interest, compute a category-wisedata-summarizing mathematical, statistical, machine learning-based, datamining-based, or empirical, etc. entities based on any of the acategory-wise average data-summarizing parameters such as those examplesdescribed above. For example:

[0172] Compute a gaussian summarizing entity _(i)G_(k) ^(sensitive) forgene k in the cell lines sensitive to drug i, i.e., _(i)G_(k)^(sensitive) (g, μ, σ)=(σ{square root}{square root over (2π)})⁻¹exp(−(g−μ)²/(2σ²)) where

[0173] μ=_(i){overscore (g)}_(k) ^(sensitive) and σ={square root}{squareroot over (s² _(i)g_(k) ^(sensitive))},

[0174] and compute analogous _(i)G_(k) ^(insensitive).

[0175] Compute discriminators, classifiers, and predictors of a , thecategory-wise biological response to external event i, but based oninformation computed from a given gene k. In these computations, weemploy as needed any of the preparatory computations described above.For example:

[0176] Compute a Bayesian probability P(jεC_(a) ^(i)|g_(k) ^(j)) that acell line j is in biological response category a due to biologicaleffect i, given the gene k abundance in cell line j, e.g.,${P( {j \in C_{i}^{\alpha}} \middle| g_{k}^{j} )} = \frac{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{\alpha} )}}{\sum\limits_{\alpha}\quad {{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{\alpha} )}}}$

[0177]_(i)G_(k) ^(a) (g_(k) ^(j))=probability of abundance value g_(k)^(j) from the gaussian density fitted to the histogram of the gene kabundances over the cell lines in response category a when subjected tobiological effect i.

[0178] A probability difference for the above probability is alsocomputed, e.g.,${{difference}_{Bayesian} = {{{P( {j \in C_{i}^{\alpha}} \middle| g_{k}^{j} )} - {w^{i,j}/{\sum\limits_{j}\quad w^{i,j}}}}}},{j \in {C_{i}^{\alpha}.}}$

[0179] Note: Importantly,difference_(Bayesian is the difference between ‘the predicted probability that cell line j is in the category a as computed from the gene k abundances across cell lines’ and ‘the observed probability that cell line j is in category a as computed from the effects of biological effect i on the cell lines’.)

[0180] As described below the determination of the likelihood of aco-occurrence was calculated using a number of differing methods,namely:

[0181] Uniform\Gaussian Discriminant Analysis—1-dimensional (UGDA 1D)

[0182] Uniform\Gaussian Discriminant Analysis—2-dimensional (UGDA 2D)

[0183] Linear Discriminant Analysis—1-dimensional (LDA 1D)

[0184] Quadratic Discriminant Analysis—1-dimensional (QDA 1D)

[0185] Linear Discriminant Analysis—2-dimensional (LDA 2D)

[0186] Quadratic Discriminant Analysis—2-dimensional (QDA 2D)

[0187] Uniform\Gaussian Discriminant Analysis—1-dimensional (UGDA 1D)

[0188] This method computes a Bayesian conditional probability P(jεC_(i)^(sensitive)|g_(k) ^(j)) that a cell line j is sensitive to drug i,given the gene k abundance g_(k) ^(j) in cell line j.

[0189] The probability is computed using the following equation:${P( {j \in C_{i}^{sensitive}} \middle| g_{k}^{j} )} = \frac{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{sensitive} )}}{\begin{matrix}{{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{sensitive} )}} +} \\{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{insensitive} )}}\end{matrix}}$

[0190] where

P(C _(i) ^(sensitive))=prior probability of the sensitive set=|C _(i)^(sensitive)|/(|C ₁ ^(sensitive) |+|C _(i) ^(insensitive)|),

P(C _(i) ^(insensitive))=prior probability of the insensitive set=|C_(i) ^(insensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

[0191]_(t)G_(k) ^(sensitive)(g_(k) ^(j))=probability of abundance valueg_(k) ^(j) from the gaussian density fitted to the histogram of the genek abundances over the sensitive cell lines when subjected to drug i.${{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} = {\frac{1}{\sigma_{k}^{sen}\sqrt{2\pi}}^{{{- {({g_{k}^{j} - \mu_{k}^{sen}})}^{2}}/2}{(\sigma_{k}^{sen})}^{2}}}},$

[0192] where

[0193] μ_(k) ^(sen)=mean of gene k abundances in the sensitive celllines, jεC_(sensitive) ^(i)

[0194] σ_(k) ^(sen)=standard deviation of gene k abundances in thesensitive cell lines, jεC_(sensitive) ^(i)

[0195]_(i)U_(k)(g_(k) ^(j))=probability of abundance value g_(k) ^(j)from the uniform density fitted to the gene k abundances over all celllines when subjected to drug i. For a given gene k, this value isconstant across all cell lines, j, i.e.,${{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} = \frac{1}{{\max ( g_{k} )} - {\min ( g_{k} )}}$

[0196] where

[0197] max(g_(k))=maximum abundance of gene k over all cell lines

[0198] min(g_(k))=minimum abundance of gene k over all cell lines

[0199] Sample parameters for the UGDA 1D for the NCI60 dataset are:

[0200] Rule 1 Gene: SID W 376472 Homo sapiens clone 24429 mRNA sequence[5′:AA041443 3′:AA041360]

[0201] Drug: Inosine-glycodialdehyde

[0202] Parameters:

μ_(k) ^(sen)=−0.4394, σ_(k) ^(sen)=0.4217

_(i) U _(k)(g _(k) ^(j))=0.2538

P(C _(i) ^(sensitive))=0.1978, P(C _(i) ^(insensitive))=0.8022

[0203] Rule 2

[0204] Gene: Human clone 23665 mRNA sequence Chr.17 [488020 (IW)5′:AA054745 3′:AA054747]

[0205] Drug: Dolastatin-10

[0206] Parameters:

μ_(k) ^(sen)=−0.7752, σ_(k) ^(sen)=0.4217

_(i) U _(k)(g _(k) ^(j))=0.2347

P(C _(i) ^(sensitive))=0.135, P(C _(i) ^(insensitive))=0.865

[0207] Rule 3

[0208] Gene: SID W 469272 Epidermal growth factor receptor [5′:AA0261753′:AA026089]

[0209] Drug: Dichloroallyl-lawsone

[0210] Parameters:

μ_(k) ^(sen)=−0.2886, σ_(k) ^(sen)=0.4416

_(i) U _(k)(g _(k) ^(j))=0.2299

P(C _(i) ^(sensitive))=0.2172, P(C _(i) ^(insensitive))=0.7828

[0211] Rule 4

[0212] Gene: ESTs Chr.1 [488132 (IW) 5′:AA047420 3′:AA047421]

[0213] Drug: N-phosphonoacetyl-L-aspartic-ac

[0214] Parameters:

μ_(k) ^(sen)=0.2863, σ_(k) ^(sen)=0.3651

_(i) U _(k)(g _(k) ^(j))=0.241

P(C _(i) ^(sensitive))=0.2583, P(C _(i) ^(insensitive))=0.7417

[0215] Rule 5

[0216] Gene: LBR Lamin B receptor Chr.1 [307225 (IW) 5′:W214683′:N93426]

[0217] Drug: Pyrazofurin

[0218] Parameters:

μ_(k) ^(sen)=0.4077, σ_(k) ^(sen)=0.4993

_(i) U _(k)(g _(k) ^(j))=0.237

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0219] Rule 6

[0220] Gene: SID W 305455 TRANSCRIPTIONAL REGULATOR ISGF3 GAMMA SUBUNIT[5′:W39053 3′:N89796]

[0221] Drug: Cyanomorpholinodoxorubicin

[0222] Parameters:

μ_(k) ^(sen)=0.4419, σ_(k) ^(sen)=0.3505

_(i) U _(k)(g _(k) ^(j))=0.2326

P(C _(i) ^(sensitive))=0.2067, P(C _(i) ^(insensitive))=0.7933

[0223] Rule 7

[0224] Gene: SID 429145 Human nicotinamide N-methyltransferase (NNMF)mRNA complete cds [5′: 3′:AA004839]

[0225] Drug: Semustine (MeCCNU)

[0226] Parameters:

μ_(k) ^(sen)=0.2891, σ_(k) ^(sen)=0.398

_(i) U _(k)(g _(k) ^(j))=0.3155

P(C _(i) ^(sensitive))=0.1606, P(C _(i) ^(insensitive))=0.8394

[0227] Rule 8

[0228] Gene: SID W 242844 ESTs Moderately similar to !!!! ALU SUBFAMILYJ WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[0229] Drug: Mitozolamide

[0230] Parameters:

μ_(k) ^(sen)=−1.008, σ_(k) ^(sen)=0.5668

_(i) U _(k)(g _(k) ^(j))=0.2381

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0231] Rule 9 Gene: *Homo sapiens lysosomal neuraminidase precursor mRNAcomplete cds SID W 487887 Hexabrachion (tenascin C cytotactin)[5′:AA046543 3′:AA045473]

[0232] Drug: Mitozolamide

[0233] Parameters:

μ_(k) ^(sen)=0.8444, σ_(k) ^(sen)=0.5358

_(i) U _(k)(g _(k) ^(j))=0.2597

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0234] Rule 10

[0235] Gene: ESTs Chr.1 [488132 (1W) 5′:AA047420 3′:AA047421]

[0236] Drug: Mitozolamide

[0237] Parameters:

μ_(k) ^(sen)=0.4755, σ_(k) ^(sen)=0.3355

_(i) U _(k)(g _(k) ^(j))=0.241

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0238] Rule 11

[0239] Gene: Human mitogen-responsive phosphoprotein (DOC-2) mRNAcomplete cds Chr.5 [428137 (IE) 5′: 3′:AA001933]

[0240] Drug: Mitozolamide

[0241] Parameters:

μ_(k) ^(sen)=0.3967, σ_(k) ^(sen)=0.3587

_(i) U _(k)(g _(k) ^(j))=0.2342

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0242] Rule 12

[0243] Gene: SID W 345420 Homo sapiens YAC clone 136A2 unknown rRNA3′untranslated region [5′:W76024 3′:W72468]

[0244] Drug: Mitozolamide

[0245] Parameters:

μ_(k) ^(sen)=0.7456, σ_(k) ^(sen)=0.5579

_(i) U _(k)(g _(k) ^(j))=0.2625

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0246] Rule 13

[0247] Gene: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW)5′:W48793 3′:W49619]

[0248] Drug: Mitozolamide

[0249] Parameters:

μ_(k) ^(sen)=0.6581, σ_(k) ^(sen)=0.3744

_(i) U _(k)(g _(k) ^(j))=0.2564

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0250] Rule 14

[0251] Gene: SID W 280376 ESTs Highly similar to CELL CYCLE PROTEINKINASE CDC5/MSD2 [Saccharomyces cerevisiae] [5′:N50317 3′:N47107]

[0252] Drug: Mitozolamide

[0253] Parameters:

μ_(k) ^(sen)=0.7347, σ_(k) ^(sen)=0.4233

_(i) U _(k)(g _(k) ^(j))=0.177

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0254] Rule 15

[0255] Gene: Human mRNA for reticulocalbin complete cds Chr. 11 [485209(1W) 5′:AA039292 3′:AA039334]

[0256] Drug: Cyclodisone

[0257] Parameters:

μ_(k) ^(sen)=0.6598, σ_(k) ^(sen)=0.2562

_(i) U _(k)(g _(k) ^(j))=0.1672

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0258] Rule 16

[0259] Gene: SID W 345420 Homo sapiens YAC clone 136A2 unknown mRNA3′untranslated region [5′:W76024 3′:W72468]

[0260] Drug: Clomesone

[0261] Parameters:

μ_(k) ^(sen)=0.7165, σ_(k) ^(sen)=0.4394

_(i) U _(k)(g _(k) ^(j))=0.2625

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0262] Rule 17

[0263] Gene: SID 289361 ESTs [5′:N99589 3′:N92652]

[0264] Drug: Fluorouracil (5FU)

[0265] Parameters:

μ_(k) ^(sen)=0.03614, σ_(k) ^(sen)=0.186

_(i) U _(k)(g _(k) ^(j))=0.2252

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[0266] Rule 18

[0267] Gene: SID 43555 MALATE OXIDOREDUCTASE [5′:H13370 3′:H06037]

[0268] Drug: Fluorouracil (5FU)

[0269] Parameters:

μ_(k) ^(sen)=0.9686, σ_(k) ^(sen)=0.4053

_(i) U _(k)(g _(k) ^(j))=0.241

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[0270] Rule 19

[0271] Gene: H.sapiens mRNA for Gal-beta(1-3/1-4)GlcNAcalpha-2.3-sialyltransferase Chr.11 [324181 (IW) 5′:W47425 3′:W47395]

[0272] Drug: Fluorouracil (5FU)

[0273] Parameters:

μ_(k) ^(sen)=0.3532, σ_(k) ^(sen)=0.2383

_(i) U _(k)(g _(k) ^(j))=0.2488

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[0274] Rule 20

[0275] Gene: ESTs Moderately similar to ZINC-BINDING PROTEIN A33[Pleurodeles waltl] Chr.16 [25718 (RW) 5′:R12025 3′:R37093]

[0276] Drug: Fluorodopan

[0277] Parameters:

μ_(k) ^(sen)=0.542, σ_(k) ^(sen)=0.2812

_(i) U _(k)(g _(k) ^(j))=0.2079

P(C _(i) ^(sensitive))=0.2061, P(C _(i) ^(insensitive))=0.7939

[0278] Rule 21

[0279] Gene: SID 470501 ESTs [5′:AA031743 3′:AA031652]

[0280] Drug: Asaley

[0281] Parameters:

μ_(k) ^(sen)=0.7867, σ_(k) ^(sen)=0.4327

_(i) U _(k)(g _(k) ^(j))=0.1869

P(C _(i) ^(sensitive))=0.1878, P(C _(i) ^(insensitive))=0.8122

[0282] Rule 22

[0283] Gene: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5′:3′:N92942]

[0284] Drug: Cyclocytidine

[0285] Parameters:

μ_(k) ^(sen)=0.004825, σ_(k) ^(sen)=0.232

_(i) U _(k)(g _(k) ^(j))=0.1835

P(C _(i) ^(sensitive))=0.2533, P(C _(i) ^(insensitive))=0.7467

[0286] Rule 23

[0287] Gene: SID W 122347 ESTs [5′:T99193 3′:T99194]

[0288] Drug: Oxanthrazole (piroxantrone)

[0289] Parameters:

μ_(k) ^(sen)=−0.09888, σ_(k) ^(sen)=0.6153

_(i) U _(k)(g _(k) ^(j))=0.2198

P(C _(i) ^(sensitive))=0.1956, P(C _(i) ^(insensitive))=0.8044

[0290] Rule 24

[0291] Gene: SID W 429290 ESTs [5′:AA007457 3′:AA007361]

[0292] Drug: Oxanthrazole piroxantrone)

[0293] Parameters:

μ_(k) ^(sen)=0.6229, σ_(k) ^(sen)=0.3177

_(i) U _(k)(g _(k) ^(j))=0.2352

P(C _(i) ^(sensitive))=0.1956, P(C _(i) ^(insensitive))=0.8044

[0294] Rule 25

[0295] Gene: ALDOC Aldolase C fructose-bisphosphate Chr.17 [229961 (IW)5′:H67774 3′:H67775]

[0296] Drug: Anthrapyrazole-derivative

[0297] Parameters:

μ_(k) ^(sen)=−0.2373, σ_(k) ^(sen)=0.3786

_(i) U _(k)(g _(k) ^(j))=0.2049

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0298] Rule 26

[0299] Gene: SID W 381819 Plastin 1 (I isoform) [5′:AA0592933′:AA059061]

[0300] Drug: Teniposide

[0301] Parameters:

μ_(k) ^(sen)=0.05147, σ_(k) ^(sen)=0.3839

_(i) U _(k)(g _(k) ^(j))=0.2101

P(C _(i) ^(sensitive))=0.1894, P(C _(i) ^(insensitive))=0.8106

[0302] Rule 27

[0303] Gene: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[0304] Drug: Daunorubicin

[0305] Parameters:

μ_(k) ^(sen)=0.918, σ_(k) ^(sen)=0.3704

_(i) U _(k)(g _(k) ^(j))=0.2762

P(C _(i) ^(sensitive))=0.1811 P(C _(i) ^(insensitive))=0.8189

[0306] Rule 28

[0307] Gene: SID 234072 EST Highly similar to RETROVIRUS-RELATED POLPOLYPROTEIN [Homo sapiens] [5′: 3′:H69001]

[0308] Drug: Aphidicolin-glycinate

[0309] Parameters:

μ_(k) ^(sen)=−0.3626, σ_(k) ^(sen)=0.4252

_(i) U _(k)(g _(k) ^(j))=0.207

P(C _(i) ^(sensitive))=0.1994 P(C _(i) ^(insensitive))=0.8006

[0310] Rule 29

[0311] Gene: SID 50243 ESTs [5′:H17681 3′:H17066]

[0312] Drug: CPT,10-OH

[0313] Parameters:

μ_(k) ^(sen)=0.8677, σ_(k) ^(sen)=0.5387

_(i) U _(k)(g _(k) ^(j))=0.2653

P(C _(i) ^(sensitive))=0.1856 P(C _(i) ^(insensitive))=0.8144

[0314] Rule 30

[0315] Gene: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds[5′:W79188 3′:W74434]

[0316] Drug: CPT,10-OH

[0317] Parameters:

μ_(k) ^(sen)=1.001, σ_(k) ^(sen)=0.6123

_(i) U _(k)(g _(k) ^(j))=0.2358

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[0318] Rule 31

[0319] Gene: SID W 361023 ESTs [5′:AA013072 3′:AA012983]

[0320] Drug: CPT,10-OH

[0321] Parameters:

μ_(k) ^(sen)=−0.8339, σ_(k) ^(sen)=0.6084

_(i) U _(k)(g _(k) ^(j))=0.2222

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[0322] Rule 32

[0323] Gene: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[0324] Drug: CPT

[0325] Parameters:

μ_(k) ^(sen)=0.8224, σ_(k) ^(sen)=0.5588

_(i) U _(k)(g _(k) ^(j))=0.2577

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0326] Rule 33

[0327] Gene: SID W 159512 Integrin alpha 6 [5′:H16046 3′:H15934]

[0328] Drug: CPT

[0329] Parameters:

μ_(k) ^(sen)=0.7291, σ_(k) ^(sen)=0.6557

_(i) U _(k)(g _(k) ^(j))=0.2571

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0330] Rule 34

[0331] Gene: SID W 429290 ESTs [5′:AA007457 3′:AA007361]

[0332] Drug: CPT

[0333] Parameters:

μ_(k) ^(sen)=0.7084, σ_(k) ^(sen)=0.4576

_(i) U _(k)(g _(k) ^(j))=0.2532

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0334] Rule 35

[0335] Gene: ESTs Chr.5 [487396 (IW) 5′:AA046573 3′:AA046660]

[0336] Drug: CPT

[0337] Parameters:

μ_(k) ^(sen)=0.6068, σ_(k) ^(sen)=0.3836

_(i) U _(k)(g _(k) ^(j))=0.1848

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0338] Rule 36

[0339] Gene: SID W 361023 ESTs [5′:AA013072 3′:AA012983]

[0340] Drug: CPT,20-ester (S)

[0341] Parameters:

μ_(k) ^(sen)=−0.6333, σ_(k) ^(sen)=0.554

_(i) U _(k)(g _(k) ^(j))=0.2222

P(C _(i) ^(sensitive))=0.255, P(C _(i) ^(insensitive))=0.745

[0342] Rule 37

[0343] Gene: SID W 125268 H.sapiens mRNA for human giant larvae homolog[5′:R05862 3′:R05776]

[0344] Drug: CPT,20-ester (S)

[0345] Parameters:

μ_(k) ^(sen)=−0.4871, σ_(k) ^(sen)=0.5365

_(i) U _(k)(g _(k) ^(j))=0.266

P(C _(i) ^(sensitive))=0.2844, P(C _(i) ^(insensitive))=0.7156

[0346] Rule 38

[0347] Gene: SID W 361023 ESTs [5′:AA013072 3′:AA012983]

[0348] Drug: CPT,20-ester (S)

[0349] Parameters:

μ_(k) ^(sen)=−0.608, σ_(k) ^(sen)=0.5756

_(i) U _(k)(g _(k) ^(j))=0.2222

P(C _(i) ^(sensitive))=0.2844, P(C _(i) ^(insensitive))=0.7156

[0350] Rule 39

[0351] Gene: SID W 125268 H.sapiens mRNA for human giant larvae homolog[5′:R05862 3′:R05776]

[0352] Drug: Chlorambucil

[0353] Parameters:

μ_(k) ^(sen)=−0.4569, σ_(k) ^(sen)=0.4595

_(i) U _(k)(g _(k) ^(j))=0.266

P(C _(i) ^(sensitive))=0.2206, P(C _(i) ^(insensitive))=0.7794

[0354] Rule 40

[0355] Gene: SID 381780 ESTs [5′:AA059257 3′:AA059223]

[0356] Drug: Paclitaxel—Taxol

[0357] Parameters:

μ_(k) ^(sen)=0.1618, σ_(k) ^(sen)=0.1828

_(i) U _(k)(g _(k) ^(j))=0.2053

P(C _(i) ^(sensitive))=0.1622, P(C _(i) ^(insensitive))=0.7794

[0358] Uniform\Gaussian Discriminant Analysis—2-dimensional (UGDA 2D)

[0359] This method computes a Bayesian conditional probability P(jεC_(i)^(sensitive)|g_(k) ^(j),g_(i) ^(j)) that a cell line j is sensitive todrug i, given the abundances of two genes k and i, g_(k) ^(j) and g_(i)^(j), respectively, in cell line j.

[0360] The probability is computed using the following equation:${{P( { {j \in C_{i}^{sensitive}} \middle| g_{k}^{j} ,g_{l}^{j}} )} = \frac{{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{sensitive} )}}{\begin{matrix}{{{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{sensitive} )}} +} \\{{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{insensitive} )}}\end{matrix}}},$

[0361] where

P(C _(i) ^(sensitive))=prior probability of the sensitive set=|C _(i)^(sensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

P(C _(i) ^(insensitive))=prior probability of the insensitive set=|C_(i) ^(insensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

[0362]_(i)G_(k,l) ^(sensitive)(g_(k) ^(j),g_(i) ^(j))=joint probabilityof abundance values g_(k) ^(j) and g_(i) ^(j) from the bivariategaussian density fitted to the histogram of gene k and l abundances overthe sensitive cell lines when subjected to drug i.

_(i) G _(k,l) ^(sensitive)(g_(k) ^(j),g_(i) ^(j))=${{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} = {\frac{1}{2\pi \quad \sigma_{k}^{sen}\sigma_{l}^{sen}\sqrt{1 - ( \rho_{k,l}^{sen} )^{2}}}\exp \{ \frac{- \lbrack {( \frac{g_{k}^{j} - \mu_{k}^{sen}}{\sigma_{k}^{sen}} )^{2} - {2{\rho_{k,l}^{sen}( \frac{g_{k}^{j} - \mu_{k}^{sen}}{\sigma_{k}^{sen}} )}( \frac{g_{l}^{j} - \mu_{l}^{sen}}{\sigma_{l}^{sen}} )} + ( \frac{g_{l}^{j} - \mu_{l}^{sen}}{\sigma_{l}^{sen}} )^{2}} \rbrack}{2( {1 - ( \rho_{k,l}^{sen} )^{2}} )} \}}},$

[0363] where

[0364] μ_(k) ^(sen)=mean of gene k abundances over the sensitive celllines

[0365] σ_(k) ^(sen)=standard deviation of gene k abundances in thesensitive cell lines

[0366] μ_(i) ^(sen)=mean of gene l abundances over the sensitive celllines

[0367] σ_(l) ^(sen)=standard deviation of gene l abundances in thesensitive cell lines

[0368] ρ_(k,l) ^(sen)=correlation coefficient of gene k and gene labundances in the sensitive cell lines

[0369]_(i)U_(k,l)(g_(k) ^(j),g_(l) ^(j))=probability of abundance valuesg_(k) ^(j) and g_(l) ^(j) from the uniform density fitted to gene k andgene l abundances over all cell lines when subjected to drug i. Forgiven genes k and l, this value is constant across all cell lines, j.${{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} = \frac{1}{\lbrack {{\max ( g_{k} )} - {\min ( g_{k} )}} \rbrack \cdot \lbrack {{\max ( g_{l} )} - {\min ( g_{l} )}} \rbrack}},$

[0370] where

[0371] max(g_(k))=maximum abundance of gene k over all cell lines

[0372] min(g_(k))=minimum abundance of gene k over all cell lines

[0373] max(g_(l))=maximum abundance of gene l over all cell lines

[0374] min(g_(l))=minimum abundance of gene l over all cell lines

[0375] Sample parameters for the UGDA 2D on the NCI60 dataset are:

[0376] Rule 1

[0377] Gene 1: SID W 116819 Homo sapiens clone 23887 mRNA sequence[5′:T93821 3′:T93776]

[0378] Gene 2: SID W 484681 Homo sapiens ES/130 mRNA complete cds[5′:AA037568 3′:AA037487]

[0379] Drug: L-Alanosine

[0380] Parameters:

μ_(k) ^(sen)=0.006423, μ_(i) ^(sen)=−0.25, σ_(k) ^(sen)=0.7146, σ_(l)^(sen)=0.4424, ρ_(k,l) ^(sen)=0.7005

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04605

P(C _(i) ^(sensitive))=0.2283, P(C _(i) ^(insensitive))=0.7717

[0381] Rule 2

[0382] Gene 1: EST Chr.6 [72745 (R) 5′:T50815 3′:T50661]

[0383] Gene 2: ESTs Weakly similar to dual specificity phosphatase[H.sapiens] Chr.17 [488150 (IW) 5′:AA057259 3′:AA058704]

[0384] Drug: L-Alanosine

[0385] Parameters:

μ_(k) ^(sen)=−0.3181, μ_(l) ^(sen)=−0.4347, σ_(k) ^(sen)=0.7029, σ_(l)^(sen)=0.3548, ρ_(k,l) ^(sen)=0.7733

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03881

P(C _(i) ^(sensitive))=0.2283, P(C _(i) ^(insensitive))=0.7717

[0386] Rule 3

[0387] Gene 1: SID W 469272 Epidermal growth factor receptor[5′:AA026175 3′:AA026089]

[0388] Gene 2: MICA MHC class I polypeptide-related sequence A Chr.6[290724 (R) 5′:3′:N71782]

[0389] Drug: Dichloroallyl-lawsone

[0390] Parameters:

μ_(k) ^(sen)=−0.2886, μ_(l) ^(sen)=−0.165, σ_(k) ^(sen)=0.4416, σ_(l)^(sen)=0.3495, ρ_(k,l) ^(sen)=0.6331

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03649

P(C _(i) ^(sensitive))=0.2172, P(C _(i) ^(insensitive))=0.7828

[0391] Rule 4

[0392] Gene 1: PROBABLE UBIQUITIN CARBOXYL-TERMINAL HYDROLASE Chr.6[129496 (E) 5′:R16453 3′:R14956]

[0393] Gene 2: SID W 125268 H.sapiens mRNA for human giant larvaehomolog [5′:R05862 3′:R05776]

[0394] Drug: Dichloroallyl-lawsone

[0395] Parameters:

μ_(k) ^(sen)=0.5512, μ_(l) ^(sen)=0.1164, σ_(k) ^(sen)=0.509, σ_(l)^(sen)=0.7882, ρ_(k,l) ^(sen)=0.8968

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05461

P(C _(i) ^(sensitive))=0.2172, P(C _(i) ^(insensitive))=0.7828

[0396] Rule 5

[0397] Gene 1: Human LOT1 mRNA complete cds Chr.6 [285041 (I) 5′:3′:N63378]

[0398] Gene 2: UBE2H Ubiquitin-conjugating enzyme E2H (homologous toyeast UBC8) Chr.7 [359705 (DIW) 5′:AA010909 3′:AA011300]

[0399] Drug: DUP785-brequinar

[0400] Parameters:

μ_(k) ^(sen)=0.4687, μ_(l) ^(sen)=−0.2413, σ_(k) ^(sen)=0.5604, σ_(l)^(sen)=0.6083, ρ_(k,l) ^(sen)=−0.3827

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06755

P(C _(i) ^(sensitive))=0.2694, P(C _(i) ^(insensitive))=0.7306

[0401] Rule 6

[0402] Gene 1: Human putative 32 kDa heart protein PHP32 mRNA completecds Chr.8 [417819 (EW) 5′:W88869 3′:W88662]

[0403] Gene 2: SID W 305455 TRANSCRIPTIONAL REGULATOR ISGF3 GAMMASUBUNIT [5′:W39053 3′:N89196]

[0404] Drug: Pyrazofurin

[0405] Parameters:

μ_(k) ^(sen)=−0.2413, μ_(l) ^(sen)=−0.01115, σ_(k) ^(sen)=0.3564, σ_(l)^(sen)=0.5233, ρ_(k,l) ^(sen)=−0.1372

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04906

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0406] Rule 7

[0407] Gene 1: SID W 509468 Protective protein for beta-galactosidase(galactosialidosis) [5′:AA047117 3′:AA047118]

[0408] Gene 2: SID W 214236 CD68 antigen [5′:H77807 3′:H77636]

[0409] Drug: Pyrazofurin

[0410] Parameters:

μ_(k) ^(sen)=−0.3715, μ_(l) ^(sen)=−0.2611, σ_(k) ^(sen)=0.521, σ_(l)^(sen)=0.5311, ρ_(k,l) ^(sen)=0.8032

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.5027

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0411] Rule 8

[0412] Gene 1: *Human ferritin L chain mRNA complete cds SID W 239001ESTs [5′:H67076 3′:H68158]

[0413] Gene 2: Homo sapiens mRNA for KIAA0638 protein partial cds Chr.11[470670(IW) 5′:AA031574 3′:AA031453]

[0414] Drug: Cyanomorpholinodoxorubicin

[0415] Parameters:

μ_(k) ^(sen)=0.438, μ_(l) ^(sen)=0.7537, σ_(k) ^(sen)=0.507, σ_(l)^(sen)=0.4528, ρ_(k,l) ^(sen)=−0.7846

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.0424

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7933

[0416] Rule 9

[0417] Gene 1: IL8 Interleukin 8 Chr.4 [328692 (DW) 5′:W40283 3′:W45324]

[0418] Gene 2: SID W 305455 TRANSCRIPTIONAL REGULATOR ISGF3 GAMMASUBUNIT [5′:W39053 3′:N89796]

[0419] Drug: Cyanomorpholinodoxorubicin

[0420] Parameters:

μ_(k) ^(sen)=0.856, μ_(l) ^(sen)=0.4419, σ_(k) ^(sen)=0.6623, σ_(l)^(sen)=0.3503, ρ_(k,l) ^(sen)=−0.5992

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.051

P(C _(i) ^(sensitive))=0.2067, P(C _(i) ^(insensitive))=0.7933

[0421] Rule 10

[0422] Gene 1: SID 272143 ESTs [5′: 3′:N35476]

[0423] Gene 2: SID W 345420 Homo sapiens YAC clone 136A2 unknown mRNA3′untranslated region [5′:W76024 3′:W72468]

[0424] Drug: Lomustine (CCNU)

[0425] Parameters:

μ_(k) ^(sen)=0.3141, μ_(l) ^(sen)=0.4027, σ_(k) ^(sen)=0.5301, σ_(l)^(sen)=0.4267, ρ_(k,l) ^(sen)=−0.9555

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04943

P(C _(i) ^(sensitive))=0.1067, P(C _(i) ^(insensitive))=0.8933

[0426] Rule 11

[0427] Gene 1: ESTs Chr.11 [345012 (IW) 5′:W76307 3′:W72280]

[0428] Gene 2: SID 429145 Human nicotinamide N-methyltransferase (NNMT)mRNA complete cds [5′: 3′:AA004839]

[0429] Drug: Semustine (MeCCNU)

[0430] Parameters:

μ_(k) ^(sen)=0.1845, μ_(l) ^(sen)=0.2891, σ_(k) ^(sen)=0.3375, σ_(l)^(sen)=0.398, ρ_(k,l) ^(sen)=0.6251

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06712

P(C _(i) ^(sensitive))=0.1066, P(C _(i) ^(insensitive))=0.8934

[0431] Rule 12

[0432] Gene 1: INPP1 Inositol polyphosphate-1-phosphatase Chr.2 [183876(EW) 5′:H30231 3′:H26976]

[0433] Gene 2: SID 429145 Human nicotinamide N-methyltransferase (NNMT)mRNA complete cds [5′: 3′:AA004839]

[0434] Drug: Semustine (MeCCNU)

[0435] Parameters:

μ_(k) ^(sen)=0.06554, μ_(l) ^(sen)=0.2891, σ_(k) ^(sen)=0.5184, σ_(l)^(sen)=0.398, ρ_(k,l) ^(sen)=−0.6708

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05885

P(C _(i) ^(sensitive))=0.1606, P(C _(i) ^(insensitive))=0.8394

[0436] Rule 13

[0437] Gene 1: SID 276915 ESTs [5′:N48564 3′:N39452]

[0438] Gene 2: SID 301144 ESTs [5′:W16630 3′:N78729]

[0439] Drug: Mitozolamide

[0440] Parameters:

μ_(k) ^(sen)=0.001165, μ_(l) ^(sen)=0.7785, σ_(k) ^(sen)=0.4, σ_(l)^(sen)=0.2994, ρ_(k,l) ^(sen)−0.3594

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04824

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0441] Rule 14

[0442] Gene 1: ESTs Chr.1 [45747 (D) 5′:H08940 3′:H08856]

[0443] Gene 2: Human mitogen-responsive phosphoprotein (DOC-2) mRNAcomplete cds Chr.5 [428137 (IE) 5′: 3′:AA001933]

[0444] Drug: Mitozolamide

[0445] Parameters:

μ_(k) ^(sen)=−0.2316, μ_(l) ^(sen)=0.3967, σ_(k) ^(sen)=0.4407, σ_(l)^(sen)=0.3587, ρ_(k,l) ^(sen)=−0.6006

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05485

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0446] Rule 15

[0447] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[0448] Gene 2: ESTs Chr.1 [488132 (IW) 5′:AA047420 3′:AA047421]

[0449] Drug: Mitozolamide

[0450] Parameters:

μ_(k) ^(sen)=−1.008, μ_(l) ^(sen)=0.4755, σ_(k) ^(sen)=0.5668, σ_(l)^(sen)=0.3355, ρ_(k,l) ^(sen)=0.3703

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05737

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0451] Rule 16

[0452] Gene 1: ESTs Chr.1 [488132 (IW) 5′:AA047420 3′:AA047421]

[0453] Gene 2: ESTs Chr.1 [346583 (IRW) 5′:W79544 3′:W74533]

[0454] Drug: Mitozolamide

[0455] Parameters:

μ_(k) ^(sen)=0.4755, μ_(l) ^(sen)=0.4998, σ_(k) ^(sen)=0.3355, σ_(l)^(sen)=0.593, ρ_(k,l) ^(sen)=0.612

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06478

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0456] Rule 17

[0457] Gene 1: SID 276915 ESTs [5′:N48564 3′:N39452]

[0458] Gene 2: SID W 487878 SPARC/osteonectin [5′:AA046533 3′:AA045463]

[0459] Drug: Mitozolamide

[0460] Parameters:

μ_(k) ^(sen)=0.001165, μ_(l) ^(sen)=0.9224, σ_(k) ^(sen)=0.4, σ_(l)^(sen)=0.4976, ρ_(k,l) ^(sen)=−0.3656

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04927

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0461] Rule 18

[0462] Gene 1: *Human ferritin L chain mRNA complete cds SID W 239001ESTs [5′:H67076 3′:H68158]

[0463] Gene 2: SID W 242844 ESTs Moderately similar to !! !! ALUSUBFAMILY J WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[0464] Drug: Mitozolamide

[0465] Parameters:

μ_(k) ^(sen)=0.5746, μ_(l) ^(sen)=−1.008, σ_(k) ^(sen)=0.4099, σ_(l)^(sen)=0.5668, ρ_(k,l) ^(sen)=0.3637

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04724

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0466] Rule 19

[0467] Gene 1: *Human ferritin L chain mRNA complete cds SID W 239001ESTs [5′:H67076 3′:H68158]

[0468] Gene 2: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW)5′:W48793 3′:W49619]

[0469] Drug: Mitozolamide

[0470] Parameters:

μ_(k) ^(sen)=0.5746, μ_(l) ^(sen)=0.6581, σ_(k) ^(sen)=0.4099, σ_(l)^(sen)=0.3744, ρ_(k,l) ^(sen)=−0.04564

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05088

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0471] Rule 20

[0472] Gene 1: SID 417008 ESTs Weakly similar to No definition linefound [C.elegans] [5′:3′:W87796]

[0473] Gene 2: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW)5′:W48793 3′:W49619]

[0474] Drug: Mitozolamide

[0475] Parameters:

μ_(k) ^(sen)=0.3847, μ_(l) ^(sen)=0.6581, σ_(k) ^(sen)=0.4824, σ_(l)^(sen)=0.3744, ρ_(k,l) ^(sen)=0.6278

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05309

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0476] Rule 21

[0477] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[0478] Gene 2: SD W 323824 NADH-CYTOCHROME B5 REDUCTASE [5′:W462113′:W46212]

[0479] Drug: Mitozolamide

[0480] Parameters:

μ_(k) ^(sen)=−1.008, μ_(l) ^(sen)=0.2421, σ_(k) ^(sen)=0.5668, σ_(l)^(sen)=0.4385, ρ_(k,l) ^(sen)=0.04634

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05737

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0481] Rule 22

[0482] Gene 1: SID 122022-[5′:T98316 3′:T98261]

[0483] Gene 2: *Homo sapiens lysosomal neuraminidase precursor mRNAcomplete cds SID W 487887 Hexabrachion (tenascin C cytotactin)[5′:AA046543 3′:AA045473]

[0484] Drug: Mitozolamide

[0485] Parameters:

μ_(k) ^(sen)=0.1567, μ_(l) ^(sen)=0.8444, ρ_(k) ^(sen)=0.4277, σ_(l)^(sen)=0.5358, ρ_(k,l) ^(sen)=0.6386

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.0423

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0486] Rule 23

[0487] Gene 1: SID W 488691 ESTs Highly similar to NODULATION PROTEIN G[Rhizobium meliloti] [5′:AA045967 3′:AA045833]

[0488] Gene 2: ESTs Chr.7 [28051 (D) 5′:R13146 3′:R40626]

[0489] Drug: Mitozolamide

[0490] Parameters:

μ_(k) ^(sen)=−0.4283, μ_(l) ^(sen)=0.6206, σ_(k) ^(sen)=0.6985, σ_(l)^(sen)=0.4756, ρ_(k,l) ^(sen)=−0.9223

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05016

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0491] Rule 24

[0492] Gene 1: Human DNA sequence from clone 1409 on chromosomeXp11.1-11.4. Contains a Inter-Alpha-Trypsin Inh Chr.X [485194 (I)5′:AA039416 3′:AA039316]

[0493] Gene 2: Human mRNA for reticulocalbin complete cds Chr. 11[485209(IW) 5′:AA039292 3′:AA039334]

[0494] Drug: Cyclodisone

[0495] Parameters:

μ_(k) ^(sen)=0.2487, μ_(l) ^(sen)=0.6598, σ_(k) ^(sen)=0.04569, σ_(l)^(sen)=0.2562, ρ_(k,l) ^(sen)=−0.4186

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03818

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.8311

[0496] Rule 25

[0497] Gene 1: Human mRNA for reticulocalbin complete cds Chr. 11[485209 (IW) 5′:AA039292 3′:AA039334]

[0498] Gene 2: SID 147338 ESTs [5′: 3′:H01302]

[0499] Drug: Cyclodisone

[0500] Parameters:

μ_(k) ^(sen)=0.6598, μ_(l) ^(sen)=0.1958, σ_(k) ^(sen)=0.2562, σ_(l)^(sen)=0.3673, ρ_(k,l) ^(sen)=−0.6593

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03137

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0501] Rule 26

[0502] Gene 1: Human GDP-dissociation inhibitor protein (Ly-GDI) mRNAcomplete cds Chr.12 [487374 (IW) 5′:AA046482 3′:AA046695]

[0503] Gene 2: Human mRNA for reticulocalbin complete cds Chr.11[485209(IW) 5′:AA039292 3′:AA039334]

[0504] Drug: Cyclodisone

[0505] Parameters:

μ_(k) ^(sen)=−0.2079, μ_(l) ^(sen)=0.6598, σ_(k) ^(sen)=0.5996, σ_(l)^(sen)=0.2565, ρ_(k,l) ^(sen)=−0.7022

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03853

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0506] Rule 27

[0507] Gene 1: SID W 510182 H.sapiens mRNA for kinase A anchor protein[5′:AA053156 3′:AA053135]

[0508] Gene 2: SID W 346663 ESTs [5′:W94188 3′:W74616]

[0509] Drug: Cyclodisone

[0510] Parameters:

μ_(k) ^(sen)=−0.4516, μ_(l) ^(sen)=0.3877, σ_(k) ^(sen)=0.4114, σ_(l)^(sen)=0.3607, ρ_(k,l) ^(sen)=−0.8186

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03563

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0511] Rule 28

[0512] Gene 1: Homo sapiens clone 24560 unknown mRNA complete cds Chr.16 [418227 (IW 5′:W90284 3′:W90607]

[0513] Gene 2: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[0514] Drug: Cyclodisone

[0515] Parameters:

μ_(k) ^(sen)=0.2463, μ_(l) ^(sen)=0.6598, σ_(k) ^(sen)=0.3831, σ_(l)^(sen)=0.2562, ρ_(k,l) ^(sen)=0.5841

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03311

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0516] Rule 29

[0517] Gene 1: ESTs Chr.1 [488132 (IW) 5′:AA047420 3′:AA047421]

[0518] Gene 2: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[0519] Drug: Cyclodisone

[0520] Parameters:

μ_(k) ^(sen)=0.479, μ_(l) ^(sen)=0.6598, σ_(k) ^(sen)=0.3464, σ_(l)^(sen)=0.2562, ρ_(k,l) ^(sen)=−0.4896

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04029

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0521] Rule 30

[0522] Gene 1: ESTs Chr.1 [488132 (IW) 5′:AA047420 3′:AA047421]

[0523] Gene 2: ESTs Chr.1 [346583 (IRW) 5′:W79544 3′:W74533]

[0524] Drug: Cyclodisone

[0525] Parameters:

μ_(k) ^(sen)=0.479, μ_(l) ^(sen)=0.4024, σ_(k) ^(sen)=0.3464, σ_(l)^(sen)=0.5961, ρ_(k,l) ^(sen)=0.7576

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06748

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0526] Rule 31

[0527] Gene 1: SID W 510395 Ribosomal protein S16 [5′:AA0537013′:AA053681]

[0528] Gene 2: SID W 345420 Homo sapiens YAC clone 136A2 unknown mRNA3′untranslated region [5′:W76024 3′:W72468]

[0529] Drug: Clomesone

[0530] Parameters:

μ_(k) ^(sen)=−0.4557, μ_(l) ^(sen)=0.7165, σ_(k) ^(sen)=0.2618, σ_(l)^(sen)=0.4934, ρ_(k,l) ^(sen)=0.4265

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05367

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0531] Rule 32

[0532] Gene 1: ESTs Wealdy similar to GAR22 protein [H.sapiens] Chr.[51904 (E) 5′:H24408 3′:H22555]

[0533] Gene 2: SID 147338 ESTs [5′: 3′:H01302]

[0534] Drug: Clomesone

[0535] Parameters:

μ_(k) ^(sen)=0.3048, μ_(l) ^(sen)=0.1604, σ_(k) ^(sen)=0.4287, σ_(l)^(sen)=0.37, ρ_(k,l) ^(sen)=−0.7076

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03507

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0536] Rule 33

[0537] Gene 1: MSN Moesin Chr.X [486864 (IW) 5′:AA043008 3′:AA042882]

[0538] Gene 2: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[0539] Drug: Clomesone

[0540] Parameters:

μ_(k) ^(sen)=0.6791, μ_(l) ^(sen)=0.4913, σ_(k) ^(sen)=0.4486, σ_(l)^(sen)0.4435, ρ_(k,l) ^(sen)=0.8962

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03916

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0541] Rule 34

[0542] Gene 1: Homo sapiens gamma2-adaptin (G2AD) mRNA complete cdsChr.14 [415647 (IW) 5′:W78996 3′:W80537]

[0543] Gene 2: ESTs Chr.6 [146640 (I) 5′:R80056 3′:R79962]

[0544] Drug: Fluorouracil (5FU)

[0545] Parameters:

μ_(k) ^(sen)=0.3802, μ_(l) ^(sen)=0.1649, σ_(k) ^(sen)=0.419, σ_(l)^(sen)=0.7902, ρ_(k,l) ^(sen)=0.9422

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04435

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[0546] Rule 35

[0547] Gene 1: SID W 415811 ESTs [5′:W84831 3′:W84784]

[0548] Gene 2: H.sapiens mRNA for Gal-beta(1-3/1-4)GlcNAcalpha-2.3-sialyltransferase Chr.11 [324181 (IW) 5′:W47425 3′:W47395]

[0549] Drug: Fluorouracil (5FU)

[0550] Parameters:

μ_(k) ^(sen)=−0.16, μ_(l) ^(sen)=−0.3532, σ_(k) ^(sen)=0.2818 , σ_(l)^(sen)=0.2383, ρ_(k,l) ^(sen)=0.2669

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.0438

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[0551] Rule 36

[0552] Gene 1: SID 289361 ESTs [5′:N99589 3′:N92652]

[0553] Gene 2: EST Chr.1 [137318 (I) 5′: 3′:R36703]

[0554] Drug: Fluorouracil (5FU)

[0555] Parameters:

μ_(k) ^(sen)=0.03614, μ_(l) ^(sen)=−0.3758, σ_(k) ^(sen)=0.186, σ_(l)^(sen)=0.4475, ρ_(k,l) ^(sen)=−0.1074

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06362

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[0556] Rule 37

[0557] Gene 1: LAMA3 Laminin alpha 3 (nicein (150 kD) kalinin (165 kD)BM600 (150 kD) epilegrin) Chr.18 [362059 (IRW) 5′:AA001431 3′:AA001432]

[0558] Gene 2: Prostacyclin-stimulating factor [human cultured diploidfibroblast cells mRNA 1124 nt] Chr.4 [488721 (IW) 5′:AA0460783′:AA046026]

[0559] Drug: Cytarabine (araC)

[0560] Parameters:

μ_(k) ^(sen)=−0.3545, μ_(l) ^(sen)=−0.4411, σ_(k) ^(sen)=0.7334, σ_(l)^(sen)=0.5863, ρ_(k,l) ^(sen)=0.8148

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06236

P(C _(i) ^(sensitive))=0.2661, P(C _(i) ^(insensitive))=0.7339

[0561] Rule 38

[0562] Gene 1: ESTs Chr.14 [244047 (I) 5′:N45439 3′:N38807]

[0563] Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5′:3′:N92942]

[0564] Drug: Cyclocytidine

[0565] Parameters:

μ_(k) ^(sen)=0.536, μ_(l) ^(sen)=0.004825, σ_(k) ^(sen)=0.4307, σ_(l)^(sen)=0.232, ρ_(k,l) ^(sen)=0.1655

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03336

P(C _(i) ^(sensitive))=0.2553, P(C _(i) ^(insensitive))=0.7467

[0566] Rule 39

[0567] Gene 1: ESTs Chr.1 [31905 (I) 5′:R17893 3′:R43139]

[0568] Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5′:3′:N92942]

[0569] Drug: Cyclocytidine

[0570] Parameters:

[0571] ti μ_(k) ^(sen)=0.1955, μ_(l) ^(sen)=0.004825, σ_(k)^(sen)=0.7301, σ_(l) ^(sen)=0.232, ρ_(k,l) ^(sen)=0.685

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03972

P(C _(i) ^(sensitive))=0.2553, P(C _(i) ^(insensitive))=0.7467

[0572] Rule 40

[0573] Gene 1: SD W 193562 Homo sapiens nuclear autoantigen GS2NA mRNAcomplete cds [5′:H47460 3′:H47370]

[0574] Gene 2: SID 307717 Homo sapiens KIAAO430 mRNA complete cds [5′:3′:N92942]

[0575] Drug: Cyclocytidine

[0576] Parameters:

μ_(k) ^(sen)=0.3942, μ_(l) ^(sen)=0.004825, σ_(k) ^(sen)=0.7788, σ_(l)^(sen)=0.232, ρ_(k,l) ^(sen)=0.5508

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04087

P(C _(i) ^(sensitive))=0.2553, P(C _(i) ^(insensitive))=0.7467

[0577] Rule 41

[0578] Gene 1: ALDOC Aldolase C fructose-bisphosphate Chr.17 [229961(IW) 5′:H67774 3′:H67775]

[0579] Gene 2: SID 470499 Human mRNA for KIAA0249 gene complete cds[5′:AA031742 3′:AA031651]

[0580] Drug: Anthrapyrazole-derivative

[0581] Parameters:

μ_(k) ^(sen)=−0.2373, μ_(l) ^(sen)=0.4104, σ_(k) ^(sen)=0.3786, σ_(l)^(sen)=0.5297, ρ_(k,l) ^(sen)−0.7901

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05241

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0582] Rule 42

[0583] Gene 1: SID 471855 Lumican [5′: 3′:AA035657]

[0584] Gene 2: Thioredoxin Reductase mRNA-log

[0585] Drug: Menogaril

[0586] Parameters:

μ_(k) ^(sen)=−0.5946, μ_(l) ^(sen)=0.4827, σ_(k) ^(sen)=0.3149, σ_(l)^(sen)=0.4498, ρ_(k,l) ^(sen)=0.8286

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03953

P(C _(i) ^(sensitive))=0.1944, P(C _(i) ^(insensitive))=0.8056

[0587] Rule 43

[0588] Gene 1: ESTSSID 327435 [5′:W32467 3′:W19830]

[0589] Gene 2: PROBABLE TRANS-1.2-DIHYDROBENZENE-1.2-DIOLDEHYDROGENASESID 211995 [5′:H75805 3′:H68500]

[0590] Drug: Hydroxyurea

[0591] Parameters:

μ_(k) ^(sen)=−0.3875, μ_(l) ^(sen)=−0.05828, σ_(k) ^(sen)=0.3831, σ_(l)^(sen)=0.3997, ρ_(k,l) ^(sen)=0.8287

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05168

P(C _(i) ^(sensitive))=0.1944, P(C _(i) ^(insensitive))=0.8517

[0592] Rule 44

[0593] Gene 1: ESTs Chr.1 [62232 (IR) 5′:T40284 3′:T41149]

[0594] Gene 2: SID W 488455 Cathepsin D (lysosomal aspartyl protease)[5′:AA047512 3′:AA047455]

[0595] Drug: CPT,10-OH

[0596] Parameters:

μ_(k) ^(sen)=0.07749, μ_(l) ^(sen)=0.249, σ_(k) ^(sen)=0.7379, σ_(l)^(sen)=0.4558, ρ_(k,l) ^(sen)=0.6965

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05378

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[0597] Rule 45

[0598] Gene 1: SID W 417320 Plasminogen activator tissue type (t-PA)[5′:W88922 3′:W89129]

[0599] Gene 2: Homo sapiens Cyr61 mRNA complete cds Chr.1 [486700 (DIW)5′:AA044451 3′:AA044574]

[0600] Drug: CPT,10-OH

[0601] Parameters:

μ_(k) ^(sen)=0.614, μ_(l) ^(sen)=0.6231, σ_(k) ^(sen)=0.4658, σ_(l)^(sen)=0.6676, ρ_(k,l) ^(sen)=−0.7235

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05368

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[0602] Rule 46

[0603] Gene 1: ESTs Chr.6 [471083 (IW) 5′:AA034335 3′:AA033710]

[0604] Gene 2: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[0605] Drug: CPT

[0606] Parameters:

μ_(k) ^(sen)=−0.2213, μ_(l) ^(sen)=0.8224, σ_(k) ^(sen)=0.6777, σ_(l)^(sen)=0.5588, ρ_(k,l) ^(sen)=0.62

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04033

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0607] Rule 47

[0608] Gene 1: *Homo sapiens lysosomal neuraminidase precursor mRNAcomplete cds SID W 487887 Hexabrachion (tenascin C cytotactin)[5′:AA046543 3′:AA045473]

[0609] Gene 2: ESTs Weakly similar to !!!! ALU SUBFAMILY J WARNING ENTRY!!!! [H.sapiens] Chr. [21955 (I) 5′:T66210 3′:T66144]

[0610] Drug: CPT

[0611] Parameters:

μ_(k) ^(sen)=0.3188, μ_(l) ^(sen)=0.5775, σ_(k) ^(sen)=0.7221, σ_(l)^(sen)=0.5522, ρ_(k,l) ^(sen)=−0.8619

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06477

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0612] Rule 48

[0613] Gene 1: SID W 365476 Protein S (alpha) [5′:AA009419 3′:AA009723]

[0614] Gene 2: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[0615] Drug: CPT

[0616] Parameters:

μ_(k) ^(sen)=−0.03662, μ_(l) ^(sen)=0.8224, σ_(k) ^(sen)=0.6534, σ_(l)^(sen)=0.5588, ρ_(k,l) ^(sen)=−0.6764

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06166

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0617] Rule 49

[0618] Gene 1: SID 469530 H.sapiens mRNA for ragA protein [5′:3′:AA026944]

[0619] Gene 2: Homo sapiens clone 24477 mRNA sequence Chr.18 [33059(IEW) 5′:R19498 3′:R43846]

[0620] Drug: CPT

[0621] Parameters:

μ_(k) ^(sen)=0.459, μ_(l) ^(sen)=−0.2041, σ_(k) ^(sen)=0.5722, σ_(l)^(sen)0.6597, ρ_(k,l) ^(sen)=−0.8312

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.04669

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0622] Rule 50

[0623] Gene 1: SID W 469299 ETS-RELATED PROTEIN ERM [5′:AA0262053′:AA026121]

[0624] Gene 2: SID W 415693 Homo sapiens mRNA for phosphatidylinositol4-kinase complete cds [5′:W78879 3′:W84724]

[0625] Drug: CPT

[0626] Parameters:

μ_(k) ^(sen)=−0.0352, μ_(l) ^(sen)=0.664, σ_(k) ^(sen)=0.5333, σ_(l)^(sen)=0.6375, ρ_(k,l) ^(sen) =−0.8029

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.0497

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0627] Rule 51

[0628] Gene 1: SID W 488148 H.sapiens mRNA for 3!UTR of unknown protein[5′:AA057239 3′:AA058703]

[0629] Gene 2: HLA-DRB5 Major histocompatibility complex class II DRbeta 5 Chr.6 [321230 (IEW) 5′:W52918 3′:AA037380]

[0630] Drug: CPT

[0631] Parameters:

μ_(k) ^(sen)=0.8224, μ_(l) ^(sen)=−0.07462, σ_(k) ^(sen)=0.5588, σ_(l)^(sen)=0.7144, ρ_(k,l) ^(sen)=−0.8079

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05766

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0632] Rule 52

[0633] Gene 1: ESTs Chr.5 [322749 (I) 5′: 3′:W15473]

[0634] Gene 2: SID 469530 H.sapiens mRNA for ragA protein [5′:3′:AA026944]

[0635] Drug: CPT

[0636] Parameters:

μ_(k) ^(sen)=−0.02124, μ_(l) ^(sen)=0.459, σ_(k) ^(sen)=0.5919, σ_(l)^(sen)0.5722, ρ_(k,l) ^(sen)=−0.8235

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05028

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0637] Rule 53

[0638] Gene 1: SID W 159512 Integrin alpha 6 [5′:H16046 3′:H15934]

[0639] Gene 2: SID 301276 ESTs Highly similar to VALYL-TRNA SYNTHETASE[Fugu rubripes] [5′:W07581 3′:N80811]

[0640] Drug: CPT

[0641] Parameters:

μ_(k) ^(sen)=0.7291, μ_(l) ^(sen)=0.6257, σ_(k) ^(sen)=0.6557, σ_(l)^(sen)=0.6193, ρ_(k,l) ^(sen)=−0.1667

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05021

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0642] Rule 54

[0643] Gene 1: SID W 125268 H.sapiens mRNA for human giant larvaehomolog [5′:R05862 3′:R05776]

[0644] Gene 2: G6PD Glucose-6-phosphate dehydrogenase Chr.X [430251 (IW)5′:AA010317 3′:AA010382]

[0645] Drug: Chlorambucil

[0646] Parameters:

μ_(k) ^(sen)=−0.4569, μ_(l) ^(sen)=−0.2982, σ_(k) ^(sen)=0.4595, σ_(l)^(sen)=0.2945, ρ_(k,l) ^(sen)=−0.1414

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06214

P(C _(i) ^(sensitive))=0.2206, P(C _(i) ^(insensitive))=0.7794

[0647] Rule 55

[0648] Gene 1: SID W 510534 MAJOR GASTROINTESTINAL TUMOR-ASSOCIATEDPROTEIN GA733-2 PRECURSOR [5′:AA055858 3′:AA055808]

[0649] Gene 2: G6PD Glucose-6-phosphate dehydrogenase Chr.X [430251 (IW)5′:AA010317 3′:AA010382]

[0650] Drug: Chlorambucil

[0651] Parameters:

μ_(k) ^(sen)=−0.7249, μ_(l) ^(sen)=−0.2982, σ_(k) ^(sen)=0.5634, σ_(l)^(sen)=0.2945, ρ_(k,l) ^(sen)=−0.3986

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06933

P(C _(i) ^(sensitive))=0.2206, P(C _(i) ^(insensitive))=0.7794

[0652] Rule 56

[0653] Gene 1: SID 29828 ESTs [5′:R16390 3′:R42331]

[0654] Gene 2: SID W 485645 KERATIN TYPE II CYTOSKELETAL 7 [5′:AA0398173′:AA041344]

[0655] Drug: 5-Hydroxypicolinaldehyde-thiose

[0656] Parameters:

μ_(k) ^(sen)=−0.1536, μ_(l) ^(sen)=0.8712, σ_(k) ^(sen)=0.5974, σ_(l)^(sen)=0.6735, ρ_(k,l) ^(sen)=0.6716

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.03954

P(C _(i) ^(sensitive))=0.1789, P(C _(i) ^(insensitive))=0.8211

[0657] Rule 57

[0658] Gene 1: SID 381780 ESTs [5′:AA059257 3′:AA059223]

[0659] Gene 2: SID 130482 ESTs [5′:R21876 3′:R21877]

[0660] Drug: Paclitaxel—Taxol

[0661] Parameters:

μ_(k) ^(sen)=0.1618, μ_(l) ^(sen)=−0.8271, σ_(k) ^(sen)=0.1828, σ_(l)^(sen)=0.3413, ρ_(k,l) ^(sen)=−0.3935

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05375

P(C _(i) ^(sensitive))=0.1789, P(C _(i) ^(insensitive))=0.8378

[0662] Rule 58

[0663] Gene 1: SID 381780 ESTs [5′:AA059257 3′:AA059223]

[0664] Gene 2: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85PROTEINS [Gallus gallus] [5′:AA059424 3′:AA057835]

[0665] Drug: Paclitaxel—Taxol

[0666] Parameters:

μ_(k) ^(sen)=0.1618, μ_(l) ^(sen)=−0.8354, σ_(k) ^(sen)=0.1828, σ_(l)^(sen)=0.4935, ρ_(k,l) ^(sen)=−0.09957

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.06437

P(C _(i) ^(sensitive))=0.1622, P(C _(i) ^(insensitive))=0.8378

[0667] Rule 59

[0668] Gene 1: *Paired basic amino acid cleaving enzyme (furin membraneassociated receptor protein) SID W 114116 Syndecan 2 (heparan sulfateproteoglycan 1 cell surface-associated fibroglycan) [5′:T795623′:T79471]

[0669] Gene 2: SID 240167 ESTs [5′:H79634 3′:H79635]

[0670] Drug: Pyrazoloacridine

[0671] Parameters:

μ_(k) ^(sen)=−0.6405, μ_(l) ^(sen)=0.3087, σ_(k) ^(sen)=0.5377, σ_(l)^(sen)=0.4283, ρ_(k,l) ^(sen)=0.7929

_(i) U _(k,l)(g ^(j) _(k) ,g ^(j) _(l))=0.05053

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0672] Linear Discriminant Analysis—1-dimensional (LDA 1D)

[0673] This method computes a Bayesian conditional probability P(jεC_(i)^(sensitive)|g_(k) ^(j)) that a cell line j is sensitive to drug i,given the gene k abundance g_(k) ^(j) in cell line j.

[0674] The probability is computed using the following equation:${P( {j \in C_{i}^{sensitive}} \middle| g_{k}^{j} )} = \frac{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{sensitive} )}}{{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{sensitive} )}} + {{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{insensitive} )}}}$

[0675] where

P(C _(i) ^(sensitive))=prior probability of the sensitive set=|C _(i)^(senitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

P(C _(i) ^(insensitive))=prior probability of the insensitive set=|C_(i) ^(insensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

[0676]_(i)G_(k) ^(sensitive)(g_(k) ^(j))=probability of abundance valueg I from the gaussian density fitted to the histogram of the gene kabundances over the sensitive cell lines when subjected to drug i.${{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} = {\frac{1}{\sigma_{k}^{avg}\sqrt{2\pi}}^{{{- {({g_{k}^{j} - \mu_{k}^{sen}})}^{2}}/2}{(\sigma_{k}^{avg})}^{2}}}},$

[0677] where

[0678] μ_(k) ^(sen)=mean of gene k abundances in the sensitive celllines

[0679] σ_(k) ^(avg)=sensitiveinsensitive class-weighted average standarddeviation of gene k abundances in the sensitive cell lines

[0680]_(i)G_(k) ^(insensitive)(g_(k) ^(j))=probability of abundancevalue g_(k) ^(j) from the gaussian density fitted to the histogram ofthe gene k abundances over the insensitive cell lines when subjected todrug i.${{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} = {\frac{1}{\sigma_{k}^{avg}\sqrt{2\pi}}^{{{- {({g_{k}^{j} - \mu_{k}^{insen}})}^{2}}/2}{(\sigma_{k}^{avg})}^{2}}}},$

[0681] where

[0682] μ_(k) ^(insen)=mean of gene k abundances in the insensitive celllines

[0683] Sample parameters for the LDA 1D analysis on the NCI60 Datasetare set out below:

[0684] Rule 1

[0685] Gene: SID W 470947 Human scaffold protein Pbp1 mRNA complete cds[5′:AA032174 3′:AA032175]

[0686] Drug: Inosine-glycodialdehyde

[0687] Parameters:

μ_(k) ^(sen)=−0.8115

μ_(k) ^(insen)=0.2001

σ_(k) ^(avg)=0.9394

P(C _(i) ^(sensitive))=0.1978, P(C _(i) ^(insensitive))=0.8022

[0688] Rule 2

[0689] Gene: Human mRNA for reticulocalbin complete cds Chr. 11 [485209(IW) 5′:AA039292 3′:AA039334]

[0690] Drug: Inosine-glycodialdehyde

[0691] Parameters:

μ_(k) ^(sen)=−0.7618

μ_(k) ^(insen)=0.1878

σ_(k) ^(avg)=0.9598

P(C _(i) ^(sensitive))=0.1978, P(C _(i) ^(insensitive))=0.8022

[0692] Rule 3

[0693] Gene: Homo sapiens cyclin-dependent kinase inhibitor (CDKN2C)mRNA complete cds Chr. [291057 (RW) 5′:W00390 3′:N72115]

[0694] Drug: L-Alanosine

[0695] Parameters:

μ_(k) ^(sen)=−0.8435

μ_(k) ^(insen)=0.25

σ_(k) ^(avg)=0.8722

P(C _(i) ^(sensitive))=0.2283, P(C _(i) ^(insensitive))=0.7717

[0696] Rule 4

[0697]

[0698] Gene: SID W 254085 ESTs Moderately similar to synaptonemalcomplex protein [M.musculus] [5′:N71532 3′:N22165]

[0699] Drug: Baker's-soluble-antifoliate

[0700] Parameters:

μ_(k) ^(sen)=0.7847

μ_(k) ^(insen)=0.2423

σ_(k) ^(avg)=0.8539

P(C _(i) ^(sensitive))=0.2361, P(C _(i) ^(insensitive))=0.7639

[0701] Rule 5

[0702] Gene: M-PHASE INDUCER PHOSPHATASE 2 Chr.20 [179373 (EW) 5′:H504373′:H50438]

[0703] Drug: 5-6-Dihydro-5-azacytidine

[0704] Parameters:

μ_(k) ^(sen)=−0.9251

μ_(k) ^(insen)=0.2324

σ_(k) ^(avg)=0.8567

P(C _(i) ^(sensitive))=0.2011, P(C _(i) ^(insensitive))=0.7989

[0705] Rule 6

[0706] Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.11 [183950 (E)5′:H30297 3′:H28104]

[0707] Drug: Mitozolamide

[0708] Parameters:

μ_(k) ^(sen)=0.1073

μ_(k) ^(insen)=−0.2694

σ_(k) ^(avg)=0.8153

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0709] Rule 7

[0710] Gene: PTN Pleiotrophin (heparin binding growth factor 8 neuritegrowth-promoting factor 1) Chr.7 [488801 (IW) 5′:AA045053 3′:AA045054]

[0711] Drug: Mitozolamide

[0712] Parameters:

μ_(k) ^(sen)=1.019

μ_(k) ^(insen)=−0.2557

σ_(k) ^(avg)=0.8554

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0713] Rule 8

[0714] Gene: SID W 380674 ESTs [5′:AA053720 3′:AA053711]

[0715] Drug: Mitozolamide

[0716] Parameters:

μ_(k) ^(sen)=1.093

μ_(k) ^(insen)=−0.2739

σ_(k) ^(avg)=0.8441

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0717] Rule 9

[0718] Gene: Glutathoine S-Tranferase Pi-log

[0719] Drug: Mitozolamide

[0720] Parameters:

μ_(k) ^(sen)=−0.917

μ_(k) ^(insen)=0.2307

σ_(k) ^(avg)=0.8411

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0721] Rule 10

[0722] Gene: SID W 242844 ESTs Moderately similar to !!! ALU SUBFAMILY JWARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[0723] Drug: Mitozolamide

[0724] Parameters:

μ_(k) ^(sen)=−1.008

μ_(k) ^(insen)=0.2536

σ_(k) ^(avg)=0.8681

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0725] Rule 11

[0726] Gene: *Hs.648 Cut (Drosophila)-like 1 (CCAAT displacementprotein) SID W 26677 ESTs [5′:R13994 3′:R39117]

[0727] Drug: Mitozolamide

[0728] Parameters:

μ_(k) ^(sen)=0.8138

μ_(k) ^(insen)=−0.2039

σ_(k) ^(avg)=0.9103

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0729] Rule 12

[0730] Gene: SID W 488387 Exostoses (multiple) 2 [5′:AA0467863′:AA046656]

[0731] Drug: Cyclodisone

[0732] Parameters:

μ_(k) ^(sen)=1.043

μ_(k) ^(insen)=−0.2128

σ_(k) ^(avg)=0.8985

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0733] Rule 13

[0734] Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.11 [183950 (E)5′:H30297 3′:H28104]

[0735] Drug: Cyclodisone

[0736] Parameters:

μ_(k) ^(sen)=1.135

μ_(k) ^(insen)=−0.2308

σ_(k) ^(avg)=0.8251

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0737] Rule 14

[0738] Gene: SID W 487535 Human mRNA for KIAAO080 gene partial cds[5′:AA043528 3′:AA043529]

[0739] Drug: Clomesone

[0740] Parameters:

μ_(k) ^(sen)=1.184

μ_(k) ^(insen)=−0.2817

σ_(k) ^(avg)=0.829

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0741] Rule 15

[0742] Gene: PTN Pleiotrophin (heparin binding growth factor 8 neuritegrowth-promoting factor 1) Chr.7 [488801 (IW) 5′:AA045053 3′:AA045054]

[0743] Drug: Clomesone

[0744] Parameters:

μ_(k) ^(sen)=1.14

μ_(k) ^(insen)=−0.2703

σ_(k) ^(avg)=0.8309

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0745] Rule 16

[0746] Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.11 [183950 (E)5′:H30297 3′:H28104]

[0747] Drug: Clomesone

[0748] Parameters:

μ_(k) ^(sen)=1.157

μ_(k) ^(insen)=−0.2746

σ_(k) ^(avg)=0.8226

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0749] Rule 17

[0750] Gene: SID W 242844 ESTs Moderately similar to !!!! ALU SUBFAMILYJ WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[0751] Drug: Clomesone

[0752] Parameters:

μ_(k) ^(sen)=−1.079

μ_(k) ^(insen)=0.2564

σ_(k) ^(avg)=0.8587

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0753] Rule 18

[0754] Gene: SID W 487535 Human mRNA for KLAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[0755] Drug: PCNU

[0756] Parameters:

μ_(k) ^(sen)=1.081

μ_(k) ^(insen)=−0.2435

σ_(k) ^(avg)=0.8791

P(C _(i) ^(sensitive))=0.1833, P(C _(i) ^(insensitive))=0.8167

[0757] Rule 19

[0758] Gene: SID W 242844 ESTs Moderately similar to !!!! ALU SUBFAMILYJ WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064)

[0759] Drug: PCNU

[0760] Parameters:

μ_(k) ^(sen)=−1.078

μ_(k) ^(insen)=0.2427

σ_(k) ^(avg)=0.8755

P(C _(i) ^(sensitive))=0.1833, P(C _(i) ^(insensitive))=0.8167

[0761] Rule 20

[0762] Gene: PTN Pleiotrophin (heparin binding growth factor 8 neuritegrowth-promoting factor 1) Chr.7 [488801 (IW) 5′:AA045053 3′:AA045054]

[0763] Drug: PCNU

[0764] Parameters:

μ_(k) ^(sen)=1.115

μ_(k) ^(insen)=−0.2502

σ_(k) ^(avg)=0.8538

P(C _(i) ^(sensitive))=0.1833, P(C _(i) ^(insensitive))=0.8167

[0765] Rule 21

[0766] Gene: Human thymosin beta-4 mRNA complete cds Chr.20 [305890(IW)5′:W19923 3′:N91268]

[0767] Drug: Cytarabine (araC)

[0768] Parameters:

μ_(k) ^(sen)=−0.7694

μ_(k) ^(insen)=0.2788

σ_(k) ^(avg)=0.8663

P(C _(i) ^(sensitive))=0.2661, P(C _(i) ^(insensitive))=0.7339

[0769] Rule 22

[0770] Gene: SID W 291620 Restin (Reed-Steinberg cell-expressedintermediate filament-associated protein) [5′:W03421 3′:N67817]

[0771] Drug: Porfiromycin

[0772] Parameters:

μ_(k) ^(sen)=0.9491

μ_(k) ^(insen)=−0.2431

σ_(k) ^(avg)=0.8965

P(C _(i) ^(sensitive))=0.2039, P(C _(i) ^(insensitive))=0.7961

[0773] Rule 23

[0774] Gene: Human extracellular protein (S1-5) mRNA complete cds Chr.2[485875 (EW) 5′:AA040442 3′:AA040443]

[0775] Drug: Oxanthrazole (piroxantrone)

[0776] Parameters:

μ_(k) ^(sen)=1.155

μ_(k) ^(insen)=−0.2805

σ_(k) ^(avg)=0.7962

P(C _(i) ^(sensitive))=0.1956, P(C _(i) ^(insensitive))=0.8044

[0777] Rule 24

[0778] Gene: SID W 299539 Human fibroblast growth factor homologousfactor 1 (FHF-1) mRNA complete cds [5′:W05845 3′:N71102]

[0779] Drug: Oxanthrazole piroxantrone)

[0780] Parameters:

μ_(k) ^(sen)=0.9238

μ_(k) ^(insen)=−0.2254

σ_(k) ^(avg)=0.862

P(C _(i) ^(sensitive))=0.1956, P(C _(i) ^(insensitive))=0.8044

[0781] Rule 25

[0782] Gene: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[0783] Drug: Oxanthrazole (piroxantrone)

[0784] Parameters:

μ_(k) ^(sen)=0.8896

μ_(k) ^(insen)=−0.2163

σ_(k) ^(avg)=0.8858

P(C _(i) ^(sensitive))=0.1956, P(C _(i) ^(insensitive))=0.8044

[0785] Rule 26

[0786] Gene: Human extracellular protein (S1-5) mRNA complete cds Chr.2[485875 (EW) 5′:AA040442 3′:AA040443]

[0787] Drug: Anthrapyrazole-derivative

[0788] Parameters:

μ_(k) ^(sen)=1.016

μ_(k) ^(insen)=−0.2458

σ_(k) ^(avg)=0.8692

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0789] Rule 27

[0790] Gene: SID W 380674 ESTs [5′:AA053720 3′:AA053711]

[0791] Drug: Anthrapyrazole-derivative

[0792] Parameters:

μ_(k) ^(sen)=0.9038

μ_(k) ^(insen)=−0.2265

σ_(k) ^(avg)=0.8898

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0793] Rule 28

[0794] Gene: ESTs Chr.2 [365120 (IW) 5′:AA025204 3′:AA025124]

[0795] Drug: Anthrapyrazole-derivative

[0796] Parameters:

μ_(k) ^(sen)=0.9014

μ_(k) ^(insen)=−0.2264

σ_(k) ^(avg)=0.9007

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0797] Rule 29

[0798] Gene: SID 229535 [5′:H66594 3′:H66595]

[0799] Drug: Teniposide

[0800] Parameters:

μ_(k) ^(sen)=−0.9209

μ_(k) ^(insen)=0.2154

σ_(k) ^(avg)=0.9114

P(C _(i) ^(sensitive))=0.1894, P(C _(i) ^(insensitive))=0.8106

[0801] Rule 30

[0802] Gene: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[0803] Drug: Daunorubicin

[0804] Parameters:

μ_(k) ^(sen)=−1.052

μ_(k) ^(insen)=0.2324

σ_(k) ^(avg)=0.8508

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0805] Rule 31

[0806] Gene: SID W 510030 ESTs Weakly similar to N-methyl-D-aspartatereceptor glutamate-binding chain [R.norvegicus] [5′:AA0530503′:AA053392]

[0807] Drug: Daunorubicin

[0808] Parameters:

μ_(k) ^(sen)=−1.088

μ_(k) ^(insen)=0.2401

σ_(k) ^(avg)=0.8526

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0809] Rule 32

[0810] Gene: SID 260288 ESTs [5′:H97716 3′:H96798]

[0811] Drug: Daunorubicin

[0812] Parameters:

μ_(k) ^(sen)=−0.9929

μ_(k) ^(insen)=0.2192

σ_(k) ^(avg)=0.9063

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0813] Rule 33

[0814] Gene: AK1 Adenylate kinase 1 Chr.9 [488381 (IW) 5′:AA0467833′:AA046653]

[0815] Drug: Daunorubicin

[0816] Parameters:

μ_(k) ^(sen)=−0.9847

μ_(k) ^(insen)=0.2169

σ_(k) ^(avg)=0.8611

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0817] Rule 34

[0818] Gene: Homo sapiens T245 protein (T245) mRNA complete eds Chr.X[343063 (IW) 5′:W67989 3′:W68001]

[0819] Drug: Daunorubicin

[0820] Parameters:

μ_(k) ^(sen)=−1.061

μ_(k) ^(insen)=0.234

σ_(k) ^(avg)=0.8647

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0821] Rule 35

[0822] Gene: *Prothymosin alpha SID W 271976 AMINOACYLASE-1 [5′:N446873′:N35315]

[0823] Drug: Daunorubicin

[0824] Parameters:

μ_(k) ^(sen)=−1.032

μ_(k) ^(insen)=0.2284

σ_(k) ^(avg)=0.858

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0825] Rule 36

[0826] Gene: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[0827] Drug: Daunorubicin

[0828] Parameters:

μ_(k) ^(sen)=−0.918

μ_(k) ^(insen)=0.2022

σ_(k) ^(avg)=0.8758

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0829] Rule 37

[0830] Gene: Homo sapiens clone 24477 mRNA sequence Chr.18 [33059 (IEW)5′:R19498 3′:R43846]

[0831] Drug: Daunorubicin

[0832] Parameters:

μ_(k) ^(sen)=−0.966

μ_(k) ^(insen)=0.2126

σ_(k) ^(avg)=0.8952

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0833] Rule 38

[0834] Gene: SID 43609 ESTs [5′:H06454 3′:H06184]

[0835] Drug: Amsacrine

[0836] Parameters:

μ_(k) ^(sen)=0.9136

μ_(k) ^(insen)=−0.2581

σ_(k) ^(avg)=0.8733

P(C _(i) ^(sensitive))=0.22, P(C _(i) ^(insensitive))=0.78

[0837] Rule 39

[0838] Gene: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′: 3′:N99151]

[0839] Drug: CPT,10-OH

[0840] Parameters:

μ_(k) ^(sen)=−0.9086

μ_(k) ^(insen)=0.2078

σ_(k) ^(avg)=0.8915

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[0841] Rule 40

[0842] Gene: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds[5′:W79188 3′:W74434]

[0843] Drug: CPT,10-OH

[0844] Parameters:

μ_(k) ^(sen)=1.001

μ_(k) ^(insen)=−0.2285

σ_(k) ^(avg)=0.8549

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[0845] Rule 41

[0846] Gene: SID 39144 ESTs Weakly similar to Rep-8 [H.sapiens][5′:R51769 3′:R51770]

[0847] Drug: CPT,20-ester (S)

[0848] Parameters:

μ_(k) ^(sen)=−0.8367

μ_(k) ^(insen)=0.2555

σ_(k) ^(avg)=0.8798

P(C _(i) ^(sensitive))=0.2344, P(C _(i) ^(insensitive))=0.7656

[0849] Rule 42

[0850] Gene: SID W 358526 ESTs [5′:W96039 3′:W94821]

[0851] Drug: CPT,14-Cl (S)

[0852] Parameters:

μ_(k) ^(sen)=−0.8436

μ_(k) ^(insen)=0.2136

σ_(k) ^(avg)=0.9027

P(C _(i) ^(sensitive))=0.2022, P(C _(i) ^(insensitive))=0.7978

[0853] Rule 43

[0854] Gene: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Cbr.19[310021 (I) 5′: 3′:N99151]

[0855] Drug: CPT,20-acetate

[0856] Parameters:

μ_(k) ^(sen)=−0.8754

μ_(k) ^(insen)=0.1973

σ_(k) ^(avg)=0.8929

P(C _(i) ^(sensitive))=0.1833, P(C _(i) ^(insensitive))=0.8167

[0857] Rule 44

[0858] Gene: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85PROTEINS [Gallus gallus] [5′:AA059424 3′:AA057835]

[0859] Drug: CPT

[0860] Parameters:

μ_(k) ^(sen)=0.8614

μ_(k) ^(insen)=−0.3016

σ_(k) ^(avg)=0.8698

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0861] Rule 45

[0862] Gene: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[0863] Drug: CPT

[0864] Parameters:

μ_(k) ^(sen)=0.8224

μ_(k) ^(insen)=−0.2881

σ_(k) ^(avg)=0.8739

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[0865] Rule 46

[0866] Gene: ESTs Chr.19 [485804 (EW) 5′:AA040350 3′:AA040351]

[0867] Drug: CPT,20-ester (S)

[0868] Parameters:

μ_(k) ^(sen)=−0.7505

μ_(k) ^(insen)=0.2562

σ_(k) ^(avg)=0.8843

P(C _(i) ^(sensitive))=0.255, P(C _(i) ^(insensitive))=0.745

[0869] Rule 47

[0870] Gene: SID W 358526 ESTs [5′:W96039 3′:W94821]

[0871] Drug: CPT,11-formyl (RS)

[0872] Parameters:

μ_(k) ^(sen)=−1.055

μ_(k) ^(insen)=0.2536

σ_(k) ^(avg)=0.8569

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[0873] Rule 48

[0874] Gene: SID W 135118 GATA-bindingprotein3 [5′:R31441 3′:R31442]

[0875] Drug: CPT, 11 -formyl (RS)

[0876] Parameters:

μ_(k) ^(sen)=0.9817

μ_(k) ^(insen)=−0.2359

σ_(k) ^(avg)=0.9021

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[0877] Rule 49

[0878] Gene: ESTs Chr.16 [154654 (RW) 5′:R55184 3′:R55185]

[0879] Drug: CPT,11-formyl (RS)

[0880] Parameters:

μ_(k) ^(sen)=0.874

μ_(k) ^(insen)=−0.2102

σ_(k) ^(avg)=0.9112

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[0881] Rule 50

[0882] Gene: SID 43609 ESTs [5′:H06454 3′:H06184]

[0883] Drug: Mechlorethamine

[0884] Parameters:

μ_(k) ^(sen)=1.042

μ_(k) ^(insen)=−0.2493

σ_(k) ^(avg)=0.8728

P(C _(i) ^(sensitive))=0.1928, P(C _(i) ^(insensitive))=0.8072

[0885] Rule 51

[0886] Gene: SID W 133851 ESTs [5′:R28233 3′:R27977]

[0887] Drug: Triethylenemelamine

[0888] Parameters:

μ_(k) ^(sen)=−0.7551

μ_(k) ^(insen)=0.2248

σ_(k) ^(avg)=0.9176

P(C _(i) ^(sensitive))=0.2294, P(C _(i) ^(insensitive))=0.7706

[0889] Rule 52

[0890] Gene: SID W 133851 ESTs [5′:R28233 3′:R27977]

[0891] Drug: Chlorambucil

[0892] Parameters:

μ_(k) ^(sen)=−0.8278

μ_(k) ^(insen)=0.2342

σ_(k) ^(avg)=0.8901

P(C _(i) ^(sensitive))=0.2206, P(C _(i) ^(insensitive))=0.7794

[0893] Rule 53

[0894] Gene: Human mRNA for KIAA0382 gene partial cds Chr.11 [486712(IEW) 5′:AA043173 3′:AA043174]

[0895] Drug: Chlorambucil

[0896] Parameters:

μ_(k) ^(sen)=−0.8832

μ_(k) ^(insen)=0.2497

σ_(k) ^(avg)=0.8826

P(C _(i) ^(sensitive))=0.2206, P(C _(i) ^(insensitive))=0.7794

[0897] Rule 54

[0898] Gene: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW)5′:W48793 3′:W49619]

[0899] Drug: Geldanamycin

[0900] Parameters:

μ_(k) ^(sen)=−0.8842

μ_(k) ^(insen)=0.225

σ_(k) ^(avg)=0.8839

P(C _(i) ^(sensitive))=0.2033, P(C _(i) ^(insensitive))=0.7967

[0901] Rule 55

[0902] Gene: Human nicotinamide nucleotide transhydrogenase mRNA nucleargene encoding mitochondrial protein Chr. [287568 (I) 5′: 3′:N62116]

[0903] Drug: Morpholino-adriamycin

[0904] Parameters:

μ_(k) ^(sen)=−1.072

μ_(k) ^(insen)=0.2139

σ_(k) ^(avg)=0.8933

P(C _(i) ^(sensitive))=0.1661, P(C _(i) ^(insensitive))=0.8339

[0905] Rule 56

[0906] Gene: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW)5′:H01598 3′:H01495]

[0907] Drug: Amonafide

[0908] Parameters:

μ_(k) ^(sen)=1.095

μ_(k) ^(insen)=−0.2498

σ_(k) ^(avg)=0.8687

P(C _(i) ^(sensitive))=0.1861, P(C _(i) ^(insensitive))=0.8139

[0909] Rule 57

[0910] Gene: SID W 415811 ESTs [5′:W84831 3′:W84784]

[0911] Drug: Pyrazoloacridine

[0912] Parameters:

μ_(k) ^(sen)=−0.873

μ_(k) ^(insen)=0.1935

σ_(k) ^(avg)=0.8924

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[0913] Quadratic Discriminant Analysis—1-dimensional (QDA 1D)

[0914] This method computes a Bayesian conditional probability P(jεC_(i)^(sensitive)|g_(k) ^(j)) that a cell line j is sensitive to drug i,given the gene k abundance

_(k) ^(j) in cell line j.

[0915] The probability is computed using the following equation:${P( {j \in C_{i}^{sensitive}} \middle| g_{k}^{j} )} = \frac{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{sensitive} )}}{{{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{sensitive} )}} + {{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} \cdot {P( C_{i}^{insensitive} )}}}$

[0916] where

P(C _(i) ^(sensitive))=prior probability of the sensitive set=|C _(i)^(sensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(sensitive)|),

P(C _(i) ^(sensitive))=prior probability of the insensitive set=|C _(i)^(insensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(sensitive)|),

[0917]_(i)G_(k) ^(sensitive)(g_(k) ^(j))=probability of abundance valueg_(k) ^(j) from the gaussian density fitted to the histogram of the genek abundances over the sensitive cell lines when subjected to drug i.${{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} = {\frac{1}{\sigma_{k}^{avg}\sqrt{2\pi}}^{{{- {({g_{k}^{j} - \mu_{k}^{sen}})}^{2}}/2}{(\sigma_{k}^{sen})}^{2}}}},$

[0918] where

[0919] μ_(k) ^(sen)=mean of gene k abundances in the sensitive celllines

[0920] σ_(k) ^(sen)=standard deviation of gene k abundances in thesensitive cell lines

[0921]_(i) ^(G) _(k) ^(insensitive)(g_(k) ^(j))=probability of abundancevalue g_(k) ^(j) from the gaussian density fitted to the histogram ofthe gene k abundances over the insensitive cell lines when subjected todrug i.${{{{}_{}^{}{}_{}^{}}( g_{k}^{j} )} = {\frac{1}{\sigma_{k}^{insen}\sqrt{2\pi}}^{{{- {({g_{k}^{j} - \mu_{k}^{insen}})}^{2}}/2}{(\sigma_{k}^{insen})}^{2}}}},$

[0922] where

[0923] μ_(k) ^(insen)=mean of gene k abundances in the insensitive celllines

[0924] σ_(k) ^(insen)=standard deviation of gene k abundances in theinsensitive cell lines

[0925] Sample parameters for QDA1 analysis on the NCI60 dataset are:

[0926] Rule 1

[0927] Gene: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[0928] Drug: Inosine-glycodialdehyde

[0929] Parameters:

μ_(k) ^(sen)=−0.7618, σ_(k) ^(sen)=1.57

μ_(k) ^(insen)=0.1878, σ_(k) ^(insen)=0.6952

P(C _(i) ^(sensitive))=0.1978, P(C _(i) ^(insensitive))=0.8022

[0930] Rule 2

[0931] Gene: SID W 470947 Human scaffold protein Pbp1 mRNA complete cds[5′:AA032174 3′:AA032175]

[0932] Drug: Inosine-glycodialdehyde

[0933] Parameters:

μ_(k) ^(sen)=−0.8115, σ_(k) ^(sen)=1.161

μ_(k) ^(insen)=0.2001, σ_(k) ^(insen)=0.8443

P(C _(i) ^(sensitive))=0.1978, P(C _(i) ^(insensitive))=0.8022

[0934] Rule 3

[0935] Gene: SID W 254085 ESTs Moderately similar to synaptonemalcomplex protein [M.musculus] [5′:N71532 3′:N22165]

[0936] Drug: Baker's-soluble-antifoliate

[0937] Parameters:

μ_(k) ^(sen)=0.7847, σ_(k) ^(sen)=0.6875

μ_(k) ^(insen)=−0.2423, σ_(k) ^(insen)=0.8722

P(C _(i) ^(sensitive))=0.2361, P(C _(i) ^(insensitive))=0.7639

[0938] Rule 4

[0939] Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.11 [183950 (E)5′:H30297 3′:H28104]

[0940] Drug: Mitozolamide

[0941] Parameters:

μ_(k) ^(sen)=1.073, σ_(k) ^(sen)=1.284

μ_(k) ^(insen)=−0.2694, σ_(k) ^(insen)=0.6137

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0942] Rule 5

[0943] Gene: PTN Pleiotrophin (heparin binding growth factor 8 neuritegrowth-promoting factor 1) Chr.7 [488801 (IW) 5′:AA045053 3′:AA045054]

[0944] Drug: Mitozolamide

[0945] Parameters:

μ_(k) ^(sen)=1.019, σ_(k) ^(sen)=1.354

μ_(k) ^(insen)=−0.2557, σ_(k) ^(insen)=0.64

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0946] Rule 6

[0947] Gene: SID W 242844 ESTs Moderately similar to !!!! ALU SUBFAMILYJ WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[0948] Drug: Mitozolamide

[0949] Parameters:

μ_(k) ^(sen)=−1.008, σ_(k) ^(sen)=0.5668

μ_(k) ^(insen)=0.2536, σ_(k) ^(insen)=0.9027

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0950] Rule 7

[0951] Gene: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[0952] Drug: Cyclodisone

[0953] Parameters:

μ_(k) ^(sen)=0.6598, σ_(k) ^(sen)=0.2562

μ_(k) ^(insen)=−0.1341, σ_(k) ^(insen)=1.038

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0954] Rule 8

[0955] Gene: SID W 488387 Exostoses (multiple) 2 [5′:AA0467863′:AA046656]

[0956] Drug: Cyclodisone

[0957] Parameters:

μ_(k) ^(sen)=1.043, σ_(k) ^(sen)=1.087

μ_(k) ^(insen)=−0.2128, σ_(k) ^(insen)=0.8262

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[0958] Rule 9

[0959] Gene: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[0960] Drug: Clomesone

[0961] Parameters:

μ_(k) ^(sen)=1.184, σ_(k) ^(sen)=0.9042

μ_(k) ^(insen)=−0.2817, σ_(k) ^(insen)=0.7835

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0962] Rule 10

[0963] Gene: PIN Pleiotrophin (heparin binding growth factor 8 neuritegrowth-promoting factor 1) Chr.7 [488801 (IW) 5′:AA045053 3′:AA045054]

[0964] Drug: Clomesone

[0965] Parameters:

μ_(k) ^(sen)=1.14, σ_(k) ^(sen)=1.31

μ_(k) ^(insen)=−0.2703, σ_(k) ^(insen)=0.636

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0966] Rule 11

[0967] Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.1 [183950 (E)5′:H30297 3′:H28104]

[0968] Drug: Clomesone

[0969] Parameters:

μ_(k) ^(sen)=1.157, σ_(k) ^(sen)=1.312

μ_(k) ^(insen)=−0.2746, σ_(k) ^(insen)=0.6219

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[0970] Rule 12

[0971] Gene: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[0972] Drug: PCNU

[0973] Parameters:

μ_(k) ^(sen)=1.081, σ_(k) ^(sen)=1.083

μ_(k) ^(insen)=0.2435, σ_(k) ^(insen)=0.7973

P(C _(i) ^(sensitive))=0.1833, P(C _(i) ^(insensitive))=0.8167

[0974] Rule 13

[0975] Gene: SID 289361 ESTs [5′:N99589 3′:N92652]

[0976] Drug: Fluorouracil (5FU)

[0977] Parameters:

μ_(k) ^(sen)=0.03614, σ_(k) ^(sen)=0.186

μ_(k) ^(insen)=−0.007432, σ_(k) ^(insen)=1.074

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[0978] Rule 14

[0979] Gene: SID 287239 ESTs [5′: 3′:N66980]

[0980] Drug: Fluorodopan

[0981] Parameters:

μ_(k) ^(sen)=−0.1888, σ_(k) ^(sen)=1.767

μ_(k) ^(insen)=0.04924, σ_(k) ^(insen)=0.6817

P(C _(i) ^(sensitive))=0.2061, P(C _(i) ^(insensitive))=0.7939

[0982] Rule 15

[0983] Gene: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5′:3′:N92942]

[0984] Drug: Cyclocytidine

[0985] Parameters:

μ_(k) ^(sen)=0.004825, σ_(k) ^(sen)=0.232

μ_(k) ^(insen)=−0.002083, σ_(k) ^(insen)=1.151

P(C _(i) ^(sensitive))=0.2533, P(C _(i) ^(insensitive))=0.7467

[0986] Rule 16

[0987] Gene: SID W 291620 Restin (Reed-Steinberg cell-expressedintermediate filament-associated protein) [5′:W03421 3′:N67817]

[0988] Drug: Porfiromycin

[0989] Parameters:

μ_(k) ^(sen)=0.9491, σ_(k) ^(sen)=0.8827

μ_(k) ^(insen)=−0.2431, σ_(k) ^(insen)=0.8715

P(C _(i) ^(sensitive))=0.2039, P(C _(i) ^(insensitive))=0.7961

[0990] Rule 17

[0991] Gene: Human extracellular protein (S1-5) mRNA complete cds Chr.2[485875 (EW) 5′:AA040442 3′:AA040443]

[0992] Drug: Oxanthrazole (piroxantrone)

[0993] Parameters:

μ_(k) ^(sen)=1.155, σ_(k) ^(sen)=0.8967

μ_(k) ^(insen)=−0.2805, σ_(k) ^(insen)=0.7438

P(C _(i) ^(sensitive))=0.1956, P(C _(i) ^(insensitive))=0.8044

[0994] Rule 18

[0995] Gene: Human extracellular protein (S1-5) mRNA complete cds Chr.2[485875 (EW) 5′:AA040442 3′:AA040443]

[0996] Drug: Anthrapyrazole-derivative

[0997] Parameters:

μ_(k) ^(sen)=1.016, σ_(k) ^(sen)=1.089

μ_(k) ^(insen)=0.2548, σ_(k) ^(insen)=0.7749

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[0998] Rule 19

[0999] Gene: SID 229535 [5′:H66594 3′:H66595]

[1000] Drug: Teniposide

[1001] Parameters:

μ_(k) ^(sen)=−0.9209, σ_(k) ^(sen)=1.487

μ_(k) ^(insen)=0.2154, σ_(k) ^(insen)=0.6755

P(C _(i) ^(sensitive))=0.1894, P(C _(i) ^(insensitive))=0.8106

[1002] Rule 20

[1003] Gene: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1004] Drug: Daunorubicin

[1005] Parameters:

μ_(k) ^(sen)=−1.052, σ_(k) ^(sen)=1.344

μ_(k) ^(insen)=0.2324, σ_(k) ^(insen)=0.6635

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1006] Rule 21

[1007] Gene: AK1 Adenylate kinase 1 Chr.9 [488381 (IW) 5′:AA0467833′:AA046653]

[1008] Drug: Daunorubicin

[1009] Parameters:

μ_(k) ^(sen)=−0.9847, σ_(k) ^(sen)=1.33

μ_(k) ^(insen)=0.2169, σ_(k) ^(insen)=0.6847

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1010] Rule 22

[1011] Gene: SID 260288 ESTs [5′:H97716 3′:H96798]

[1012] Drug: Daunorubicin

[1013] Parameters:

μ_(k) ^(sen)=−0.9929, σ_(k) ^(sen)=1.81

μ_(k) ^(insen)=0.2192, σ_(k) ^(insen)=0.4776

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1014] Rule 23

[1015] Gene: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[1016] Drug: Daunorubicin

[1017] Parameters:

μ_(k) ^(sen)=−0.918, σ_(k) ^(sen)=0.3704

μ_(k) ^(insen)=−0.2022, σ_(k) ^(insen)=0.9271

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1018] Rule 24

[1019] Gene: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′: 3′:N99151]

[1020] Drug: CPT,10-OH

[1021] Parameters:

μ_(k) ^(sen)=−0.9086, σ_(k) ^(sen)=0.8266

μ_(k) ^(insen)=0.2078, σ_(k) ^(insen)=0.8782

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1022] Rule 25

[1023] Gene: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85PROTEINS [Gallus gallus] [5′:AA059424 3′:AA057835]

[1024] Drug: CPT

[1025] Parameters:

μ_(k) ^(sen)=0.8614, σ_(k) ^(sen)=0.8019

μ_(k) ^(insen)=−0.3016, σ_(k) ^(insen)=0.8633

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[1026] Rule 26

[1027] Gene: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[1028] Drug: CPT

[1029] Parameters:

μ_(k) ^(sen)=0.8224, σ_(k) ^(sen)=0.5588

μ_(k) ^(insen)=−0.2881, σ_(k) ^(insen)=0.9329

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[1030] Rule 27

[1031] Gene: SID W 358526 ESTs [5′:W96039 3′:W94821]

[1032] Drug: CPT,11-formyl (RS)

[1033] Parameters:

μ_(k) ^(sen)=−1.055, σ_(k) ^(sen)=1.241

μ_(k) ^(insen)=0.2536, σ_(k) ^(insen)=0.7034

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[1034] Rule 28

[1035] Gene: SID W 135118 GATA-binding protein 3 [5′:R31441 3′:R31442]

[1036] Drug: CPT,11-formyl (RS)

[1037] Parameters:

μ_(k) ^(sen)=0.9817, σ_(k) ^(sen)=1.5

μ_(k) ^(insen)=−0.2359, σ_(k) ^(insen)=0.6465

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[1038] Rule 29

[1039] Gene: SID 43609 ESTs [5′:H06454 3′:H06184]

[1040] Drug: CPT,11-formyl (RS)

[1041] Parameters:

μ_(k) ^(sen)=0.6312, σ_(k) ^(sen)=1.498

μ_(k) ^(insen)=−0.1522, σ_(k) ^(insen)=0.7671

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[1042] Rule 30

[1043] Gene: ESTs Chr.16 [154654 (RW) 5′:R55184 3′:R55185]

[1044] Drug: CPT,11-formyl (S)

[1045] Parameters:

μ_(k) ^(sen)=0.874, σ_(k) ^(sen)=1.247

μ_(k) ^(insen)=0.2102, σ_(k) ^(insen)=0.7775

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[1046] Rule 31

[1047] Gene: AK1 Adenylate kinase 1 Chr.9 [488381 (IW) 5′:AA0467833′:AA046653]

[1048] Drug: Mechlorethamine

[1049] Parameters:

μ_(k) ^(sen)=−0.4881, σ_(k) ^(sen)=1.786

μ_(k) ^(insen)=0.1157, σ_(k) ^(insen)=0.6286

P(C _(i) ^(sensitive))=0.1928, P(C _(i) ^(insensitive))=0.8072

[1050] Rule 32

[1051] Gene: SID 43609 ESTs [5′:H06454.3′:H06184]

[1052] Drug: Mechlorethamine

[1053] Parameters:

μ_(k) ^(sen)=1.042, σ_(k) ^(sen)=0.9895

μ_(k) ^(insen)=−0.2493, σ_(k) ^(insen)=0.814

P(C _(i) ^(sensitive))=0.1928, P(C _(i) ^(insensitive))=0.8072

[1054] Rule 33

[1055] Gene: SID 43609 ESTs [5′:H06454 3′:H06184]

[1056] Drug: Triethylenemelamine

[1057] Parameters:

μ_(k) ^(sen)=0.6685, σ_(k) ^(sen)=1.405

μ_(k) ^(insen)=−0.1995, σ_(k) ^(insen)=0.7269

P(C _(i) ^(sensitive))=0.2294, P(C _(i) ^(insensitive))=0.7706

[1058] Rule 34

[1059] Gene: SID W 133851 ESTs [5′:R28233 3′:R27977]

[1060] Drug: Triethylenemelamine

[1061] Parameters:

μ_(k) ^(sen)=−0.7551, σ_(k) ^(sen)=1.506

μ_(k) ^(insen)=0.2248, σ_(k) ^(insen)=0.6021

P(C _(i) ^(sensitive))=0.2294, P(C _(i) ^(insensitive))=0.7706

[1062] Rule 35

[1063] Gene: SID 43609 ESTs [5′:H06454 3′:H06184]

[1064] Drug: Thiotepa

[1065] Parameters:

μ_(k) ^(sen)=0.6796, σ_(k) ^(sen)=1.35

μ_(k) ^(insen)=−0.2073, σ_(k) ^(insen)=0.728

P(C _(i) ^(sensitive))=0.2333, P(C _(i) ^(insensitive))=0.7667

[1066] Rule 36

[1067] Gene: SID W 291620 Restin (Reed-Steinberg cell-expressedintermediate filament-associated protein) [5′:W03421 3′:N67817]

[1068] Drug: Chlorambucil

[1069] Parameters:

μ_(k) ^(sen)=−0.01776, σ_(k) ^(sen)=1.597

μ_(k) ^(insen)=0.005025, σ_(k) ^(insen)=0.7447

P(C _(i) ^(sensitive))=0.2206, P(C _(i) ^(insensitive))=0.7794

[1070] Rule 37

[1071] Gene: SID W 133851 ESTs [5′:R28233 3′:R27977]

[1072] Drug: Chlorambucil

[1073] Parameters:

μ_(k) ^(sen)=−0.8278, σ_(k) ^(sen)=1.471

μ_(k) ^(insen)=0.2342, σ_(k) ^(insen)=0.5941

P(C _(i) ^(sensitive))=0.2206, P(C _(i) ^(insensitive))=0.7794

[1074] Rule 38

[1075] Gene: SID W 510230 Homo sapiens (clone CC6) NADH-ubiquinoneoxidoreductase subunit mRNA 3′ end cds [5′:AA053568 3′:AA053557]

[1076] Drug: Geldanamycin

[1077] Parameters:

μ_(k) ^(sen)=0.1441, σ_(k) ^(sen)=1.609

μ_(k) ^(insen)=−0.03698, σ_(k) ^(insen)=0.7474

P(C _(i) ^(sensitive))=0.2033, P(C _(i) ^(insensitive))=0.7967

[1078] Rule 39

[1079] Gene: SID 381780 ESTs [5′:AA059257 3′:AA059223]

[1080] Drug: Paclitaxel—Taxol

[1081] Parameters:

μ_(k) ^(sen)=0.1618, σ_(k) ^(sen)=0.1828

μ_(k) ^(insen)=−0.03218, σ_(k) ^(insen)=1.06

P(C _(i) ^(sensitive))=0.1622, P(C _(i) ^(insensitive))=0.8378

[1082] Rule 40

[1083] Gene: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW)5′:H01598 3′:H01495]

[1084] Drug: Amonafide

[1085] Parameters:

μ_(k) ^(sen)=1.905, σ_(k) ^(sen)=1.188

μ_(k) ^(insen)=−0.2498, σ_(k) ^(insen)=0.7473

P(C _(i) ^(sensitive))=0.1861, P(C _(i) ^(insensitive))=0.8139

[1086] Linear Discriminant Analysis—2-dimensional (LDA 2D)

[1087] This method computes a Bayesian conditional probability P(jεC_(i)^(sensitive)|g_(k) ^(j), g_(l) ^(j)) that a cell line j is sensitive todrug i, given the abundances of genes k and l, g_(k) ^(j), g_(l) ^(j),respectively, in cell line j.

[1088] The probability is computed using the following equation:${{P( { {j \in C_{i}^{sensitive}} \middle| g_{k}^{j} ,g_{l}^{j}} )} = \frac{{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{sensitive} )}}{{{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{sensitive} )}} + {{{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{insensitive} )}}}},$

[1089] where

P(C _(i) ^(sensitive))=prior probability of the sensitive set=|C _(i)^(sensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

P(C _(i) ^(insensitive))=prior probability of the insensitive set=|C_(i) ^(insensitive)|/(|C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

[1090]_(i)G_(k,l) ^(sensitive)(g_(k) ^(j), g_(l) ^(j))=joint probabilityof abundance values g_(k) ^(j) and g_(l) ^(j) from the bivariategaussian density fitted to the histogram of gene k and l abundances overthe sensitive cell lines when subjected to drug i.

_(i) G _(k,l) ^(sensitive)(g _(k) ^(j) , g _(l) ^(j))=${{{}_{}^{}{}_{k,l}^{}}( {g_{k}^{j},g_{l}^{j}} )} = {\frac{1}{2\pi \quad \sigma_{k}^{avg}\sigma_{l}^{avg}\sqrt{1 - ( \rho_{k,l}^{avg} )^{2}}}\exp \{ \frac{- \lbrack {( \frac{g_{k}^{j} - \mu_{k}^{sen}}{\sigma_{k}^{avg}} )^{2} - {2{\rho_{k,l}^{avg}( \frac{g_{k}^{j} - \mu_{k}^{sen}}{\sigma_{k}^{avg}} )}( \frac{g_{l}^{j} - \mu_{l}^{sen}}{\sigma_{l}^{avg}} )} + ( \frac{g_{l}^{j} - \mu_{l}^{sen}}{\sigma_{l}^{avg}} )^{2}} \rbrack}{2( {1 - ( \rho_{k,l}^{avg} )^{2}} )} \}}$

[1091] where

[1092] μ_(k) ^(sen)=mean of gene k abundances over the sensitive celllines

[1093] σ_(k) ^(avg)=sensitive\insensitive class-weighted averagestandard deviation of gene k abundances in the sensitive and insensitivecell lines

[1094] μ_(l) ^(sen)=mean of gene 1 abundances over the sensitive celllines

[1095] σ_(l) ^(avg)=sensitive\insensitive class-weighted averagestandard deviation of gene 1 abundances in the sensitive and insensitivecell lines

[1096] ρ_(k,l) ^(avg)=sensitive\insensitive class-weighted averagecorrelation coefficient of gene k and gene l abundances in the sensitiveand insensitive cell lines

[1097]_(i)G_(k,l) ^(insensitive)(g_(k) ^(j), g_(l) ^(j))=jointprobability of abundance values g_(k) ^(j) and g_(l) ^(j) from thebivariate gaussian density fitted to the histogram of gene k and labundances over the insensitive cell lines when subjected to drug i.

_(i) G _(k,l) ^(insensitive)(g _(k) ^(j) , g _(l) ^(j))=$\quad_{i}{G_{k,l}^{insensitive}( {g_{k}^{j},g_{l}^{j}} )} = {\frac{1}{2{\pi\sigma}_{k}^{avg}\sigma_{l}^{avg}\sqrt{1 - ( \rho_{k,l}^{avg} )^{2}}}\exp \{ \frac{- \lbrack {( \frac{g_{k}^{j} - \mu_{k}^{insen}}{\sigma_{k}^{avg}} )^{2} - {2{\rho_{k,l}^{avg}( \frac{g_{k}^{j} - \mu_{k}^{insen}}{\sigma_{k}^{avg}} )}( \frac{g_{l}^{j} - \mu_{l}^{insen}}{\sigma_{l}^{avg}} )} + ( \frac{g_{l}^{j} - \mu_{l}^{insen}}{\sigma_{l}^{avg}} } }{2( {1 - ( \rho_{k,l}^{avg} )^{2}} )} }$

[1098] where

[1099] μ_(k) ^(insen) is the mean of gene k abundances over theinsensitive cell lines

[1100] μ_(l) ^(insen) is the mean of gene k abundances over theinsensitive cell lines

[1101] Sample parameters for the LDA 2D analysis on the NCI60 datasetare:

[1102] Rule 1

[1103] Gene 1: Glyoxalase-I-log

[1104] Gene 2: Homo sapiens mRNA for HYA22 complete cds Chr.3 [358957(EW) 5′:W91969 3′:W94916]

[1105] Drug: Acivicin

[1106] Parameters:

μ_(k) ^(sen)=−0.9056, μ_(l) ^(sen)=0.3517

μ_(k) ^(insen)=0.2197, μ_(l) ^(sen)=−0.08527

σ_(k) ^(avg)=0.8751, σ_(l) ^(avg)=0.9817, ρ_(k,l) ^(avg)=0.531

P(C _(i) ^(sensitive))=0.1956, P(C _(i) ^(insensitive))=0.8044

[1107] Rule 2

[1108] Gene 1: SID W 254085 ESTs Moderately similar to synaptonemalcomplex protein [M.musculus] [5′:N71532 3′:N22165]

[1109] Gene 2: SID 118593 [5′:T92821 3′:T92741]

[1110] Drug: Baker's-soluble-antifoliate

[1111] Parameters:

μ_(k) ^(sen)=0.7847, μ_(l) ^(sen)=−0.5796

μ_(k) ^(insen)=−0.2423, μ_(l) ^(insen)=0.1796

σ_(k) ^(avg)=0.8539, σ_(l) ^(avg)=0.8599, ρ_(k,l) ^(avg)=0.2493

P(C _(i) ^(sensitive))=0.2361, P(C _(i) ^(insensitive))=0.7639

[1112] Rule 3

[1113] Gene 1: SID W 254085 ESTs Moderately similar to synaptonemalcomplex protein [M.musculus] [5′:N71532 3′:N22165]

[1114] Gene 2: ESTs Chr.5 [46694 (RW) 5′:H10240.3′:H10192]

[1115] Drug: Baker's-soluble-antifoliate

[1116] Parameters:

μ_(k) ^(sen)=0.7847, μ_(l) ^(sen)=−0.4403

μ_(k) ^(insen)=−0.2423, μ_(l) ^(insen)=0.1363

σ_(k) ^(avg)=0.8539, σ_(l) ^(avg)=0.9706, ρ_(k,l) ^(avg)=0.1844

P(C _(i) ^(sensitive))=0.2361, P(C _(i) ^(insensitive))=0.7639

[1117] Rule 4

[1118] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1119] Gene 2: *Hs.648 Cut (Drosophila)-like 1 (CCAAT displacementprotein) SID W 26677 ESTs [5′:R13994 3′:R39117]

[1120] Drug: Mitozolamide

[1121] Parameters:

μ_(k) ^(sen)=−1.008, μ_(l) ^(sen)=0.8138

μ_(k) ^(insen)=0.2536, μ_(l) ^(insen)=−0.2039

σ_(k) ^(avg)=0.8681, σ_(l) ^(avg)=0.9103, ρ_(k,l) ^(avg)=0.07755

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1122] Rule 5

[1123] Gene 1: Homo sapiens delta7-sterol reductase mRNA complete cdsChr.10 [417125 (E) 5′:3′:W87472]

[1124] Gene 2: SID W 380674 ESTs [5′:AA053720 3′:AA053711]

[1125] Drug: Mitozolamide

[1126] Parameters:

μ_(k) ^(sen)=−0.7211, μ_(l) ^(sen)=1.093

μ_(k) ^(insen)=0.1813, μ_(l) ^(insen)=−0.2739

σ_(k) ^(avg)=0.9411, σ_(l) ^(avg)=0.8441, ρ_(k,l) ^(avg)=0.1253

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1127] Rule 6

[1128] Gene 1: Glutathoine S-Tranferase Pi-log

[1129] Gene 2: *Hs.648 Cut (Drosophila)-like 1 (CCAAT displacementprotein) SID W 26677 ESTs [5′:R13994 3′:R39117]

[1130] Drug: Mitozolamide

[1131] Parameters:

μ_(k) ^(sen)=−0.917, μ_(l) ^(sen)=0.8138

μ_(k) ^(insen)=0.2307, μ_(l) ^(insen)=−0.2039

σ_(k) ^(avg)=0.8411, σ_(l) ^(avg)=0.9103, ρ_(k,l) ^(avg)=0.04772

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1132] Rule 7

[1133] Gene 1: ESTs Chr.X [48536 (E) 5′:H14669 3′:H14579]

[1134] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[1135] Drug: Clomesone

[1136] Parameters:

μ_(k) ^(sen)=−0.8957, μ_(l) ^(sen)=−1.079

μ_(k) ^(insen)=0.2117, μ_(l) ^(insen)=0.2564

σ_(k) ^(avg)=0.8904, σ_(l) ^(avg)=0.8587, ρ_(k,l) ^(avg)=−0.165

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1137] Rule 8

[1138] Gene 1: SID W 36809 Homo sapiens neural cell adhesion molecule(CALL) mRNA complete cds [5′:R34648 3′:R49177]

[1139] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1140] Drug: Clomesone

[1141] Parameters:

μ_(k) ^(sen)=0.6335, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=−0.1498, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9603, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=−0.2448

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1142] Rule 9

[1143] Gene 1: M-PHASE INDUCER PHOSPHATASE 2 Chr.20 [179373 (EW)5′:H50437 3′:H50438]

[1144] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1145] Drug: Clomesone

[1146] Parameters:

μ_(k) ^(sen)=0.3874, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=−0.9229, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9766, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=0.2704

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1147] Rule 10

[1148] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1149] Gene 2: SID 469842 Homo sapiens mRNA for fatty acid bindingprotein complete cds [5′:AA029794 3′:AA029795]

[1150] Drug: Clomesone

[1151] Parameters:

μ_(k) ^(sen)=−1.079, μ_(l) ^(sen)=0.8757

μ_(k) ^(insen)=0.2564, μ_(l) ^(insen)=−0.2074

σ_(k) ^(avg)=0.8587, σ_(l) ^(avg)=0.9151, ρ_(k,l) ^(avg)=0.1636

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1152] Rule 11

[1153] Gene 1: ESTsSID 327435 [5′:W32467 3′:W19830]

[1154] Gene 2: SID 469842 Homo sapiens mRNA for fatty acid bindingprotein complete cds [5′:AA029794 3′:AA029795]

[1155] Drug: Clomesone

[1156] Parameters:

μ_(k) ^(sen)=−0.793, μ_(l) ^(sen)=0.8757

μ_(k) ^(insen)=0.1878, μ_(l) ^(insen)=−0.2074

σ_(k) ^(avg)=0.9388, σ_(l) ^(avg)=0.9151, ρ_(k,l) ^(avg)=0.4476

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1157] Rule 12

[1158] Gene 1: SID 512164 Human clathrin assembly protein 50 (AP50) mRNAcomplete cds [5′:3′:AA057396]

[1159] Gene 2: SID W 345624 Human homeobox protein (PHOX1) mRNA 3′ end[5′:W76402 3′:W72050]

[1160] Drug: Clomesone

[1161] Parameters:

μ_(k) ^(sen)=0.8248, μ_(l) ^(sen)=−0.253

μ_(k) ^(insen)=−0.1956, μ_(l) ^(insen)=0.06021

σ_(k) ^(avg)=0.9014, σ_(l) ^(avg)=1.015, ρ_(k,l) ^(avg)=0.72

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1162] Rule 13

[1163] Gene 1: SID W 376951 ESTs [5′:AA047756 3′:AA047641]

[1164] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1165] Drug: Clomesone

[1166] Parameters:

μ_(k) ^(sen)=0.8665, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=−0.2063, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9396, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=0.1106

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1167] Rule 14

[1168] Gene 1: Glutathoine S-Tranferase Pi-log

[1169] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1170] Drug: Clomesone

[1171] Parameters:

μ_(k) ^(sen)=−0.8961, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=0.2131, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.8991, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=0.1075

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1172] Rule 15

[1173] Gene 1: XRCC4 DNA repair protein XRCC4 Chr.5 [26811 (RW)5′:R14027 3′:R39148]

[1174] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[1175] Drug: Clomesone

[1176] Parameters:

μ_(k) ^(sen)=−0.583, μ_(l) ^(sen)=−1.079

μ_(k) ^(insen)=0.1387, μ_(l) ^(insen)=0.2564

σ_(k) ^(avg)=0.9879, σ_(l) ^(avg)=0.8587, ρ_(k,l) ^(avg)=−0.3373

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1177] Rule 16

[1178] Gene 1: Homo sapiens clone 24711 mRNA sequence Chr.2 [345084 (IW)5′:W76362 3′:W72306]

[1179] Gene 2: *Homo sapiens lysosomal neuraminidase precursor mRNAcomplete cds SID W 487887 Hexabrachion (tenascin C cytotactin)[5′:AA046543 3′:AA045473]

[1180] Drug: Clomesone

[1181] Parameters:

μ_(k) ^(sen)=−0.5805, μ_(l) ^(sen)=0.8678

μ_(k) ^(insen)=0.137, μ_(l) ^(insen)=−0.2056

σ_(k) ^(avg)=0.968, σ_(l) ^(avg)=0.911, ρ_(k,l) ^(avg)=0.5627

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1182] Rule 17

[1183] Gene 1: SID 260048 Homo sapiens intermediate conductancecalcium-activated potassium channel (hKCa4) mRNA complete [5′:3′:N32010]

[1184] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1185] Drug: Clomesone

[1186] Parameters:

μ_(k) ^(sen)=0.3774, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=−0.09052, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=1.015, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=−0.2375

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1187] Rule 18

[1188] Gene 1: ESTs Weakly similar to R06B9.b [C.elegans] Chr.1 [365488(IW) 5′:AA009557 3′:AA009558]

[1189] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1190] Drug: Clomesone

[1191] Parameters:

μ_(k) ^(sen)=0.6026, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=−0.1433, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9451, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=−0.0427

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1192] Rule 19

[1193] Gene 1: ESTs Moderately similar to DUAL SPECIFICITY PROTEINPHOSPHATASE VHR [H.sapiens] Chr.17 [49293 (E) 5′:H15616 3′:H15557]

[1194] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1195] Drug: Clomesone

[1196] Parameters:

μ_(k) ^(sen)=−0.1122, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=0.02618, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=1.019, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=0.4234

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1197] Rule 20

[1198] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[1199] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1200] Drug: Clomesone

[1201] Parameters:

μ_(k) ^(sen)=−1.079, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=0.2564, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.8587, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=0.02375

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1202] Rule21

[1203] Gene 1: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1204] Gene 2: ESTs Chr.6 [144805 (EW) 5′:R76279 3′:R76556]

[1205] Drug: Clomesone

[1206] Parameters:

μ_(k) ^(sen)=1.184, μ_(l) ^(sen)=0.4822

μ_(k) ^(insen)=−0.2817, μ_(l) ^(insen)=−0.1143

σ_(k) ^(avg)=0.829, σ_(l) ^(avg)=0.9949, ρ_(k,l) ^(avg)=−0.2002

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1207] Rule 22

[1208] Gene 1: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1209] Gene 2: SID W 488333 ESTs [5′:AA046755 3′:AA046642]

[1210] Drug: Clomesone

[1211] Parameters:

μ_(k) ^(sen)=1.184, μ_(l) ^(sen)=−0.1604

μ_(k) ^(insen)=−0.2817, μ_(l) ^(insen)=0.03825

σ_(k) ^(avg)=0.829, σ_(l) ^(avg)=1.011, ρ_(k,l) ^(avg)=0.3461

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1212] Rule 23

[1213] Gene 1: ANX3 Annexin III (lipocortin III) Chr.4 [328683 (IW)5′:W40286 3′:W45327]

[1214] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1215] Drug: Clomesone

[1216] Parameters:

μ_(k) ^(sen)=−0.7239, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=0.1714, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9663, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=−0.1129

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1217] Rule 24

[1218] Gene 1: SID 308729 ESTs [5′:W25229 3′:N95389]

[1219] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1220] Drug: Clomesone

[1221] Parameters:

μ_(k) ^(sen)=−0.6074, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=0.1438, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9876, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=0.1155

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1222] Rule 25

[1223] Gene 1: Metallothionein content-log

[1224] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1225] Drug: Clomesone

[1226] Parameters:

μ_(k) ^(sen)=0.5109, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=−0.1211, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9435, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=−0.3179

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1227] Rule 26

[1228] Gene 1: ESTs Chr.14 [160605 (E) 5′:H25013 3′:H25014]

[1229] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1230] Drug: Clomesone

[1231] Parameters:

μ_(k) ^(sen)=−0.7174, μ_(l) ^(sen)=1.184

μ_(k) ^(insen)=0.1703, μ_(l) ^(insen)=−0.2817

σ_(k) ^(avg)=0.9506, σ_(l) ^(avg)=0.829, ρ_(k,l) ^(avg)=0.01308

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1232] Rule 27

[1233] Gene 1: SID W 510534 MAJOR GASTROINTESTINAL TUMOR-ASSOCIATEDPROTEIN GA733-2 PRECURSOR [5′:AA055858 3′:AA055808]

[1234] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1235] Drug: Clomesone

[1236] Parameters:

μ_(k) ^(sen)=−0.867, μ_(l) ^(sen)=−1.079

μ_(k) ^(insen)=0.2052, μ_(l) ^(insen)=0.2564

σ_(k) ^(avg)=0.9304, σ_(l) ^(avg)=0.8587, ρ_(k,l) ^(avg)=−0.08247

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1237] Rule 28

[1238] Gene 1: SID W 489262 Allograft inflammatory factor 1 [5′:AA0457183′:AA045719]

[1239] Gene 2: SID W 489301 ESTs [5′:AA054471 3′:AA058511]

[1240] Drug: PCNU

[1241] Parameters:

μ_(k) ^(sen)=−0.1844, μ_(l) ^(sen)=0.7991

μ_(k) ^(insen)=0.04227, μ_(l) ^(insen)=−0.1796

σ_(k) ^(avg)=0.9895, σ_(l) ^(avg)=0.9465, ρ_(k,l) ^(avg)=0.7317

P(C _(i) ^(sensitive))=0.1833, P(C _(i) ^(insensitive))=0.8167

[1242] Rule 29

[1243] Gene 1: p53 mutation-log

[1244] Gene 2: SID 43555 MALATE OXIDOREDUCTASE [5′:H13370 3′:H06037]

[1245] Drug: Fluorouracil (5FU)

[1246] Parameters:

μ_(k) ^(sen)=0.9274, μ_(l) ^(sen)=0.9686

μ_(k) ^(insen)=−0.1772, μ_(l) ^(insen)=−0.1883

σ_(k) ^(avg)=0.899, σ_(l) ^(avg)=0.9219, ρ_(k,l) ^(avg)=−0.186

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[1247] Rule 30

[1248] Gene 1: ME2 Malic enzyme 2 mitochondrial Chr.18 [109375 (IW)5′:T80865 3′:T70290]

[1249] Gene 2: SID W 488806 Thioredoxin [5′:AA045051 3′:AA045052]

[1250] Drug: Asaley

[1251] Parameters:

μ_(k) ^(sen)=0.7873, μ_(l) ^(sen)=−0.922

μ_(k) ^(insen)=−0.182, μ_(l) ^(insen)=0.2136

σ_(k) ^(avg)=0.9409, σ_(l) ^(avg)=0.9102, ρ_(k,l) ^(avg)=0.3849

P(C _(i) ^(sensitive))=0.1878, P(C _(i) ^(insensitive))=0.8122

[1252] Rule 31

[1253] Gene 1: X-ray induction of mdm2-log

[1254] Gene 2: Human thymosin beta-4 mRNA complete cds Chr.20 [305890(IW) 5′:W19923 3′:N91268]

[1255] Drug: Cytarabine (araC)

[1256] Parameters:

μ_(k) ^(sen)=0.5649, μ_(l) ^(sen)=−0.7694

μ_(k) ^(insen)=−0.2054, μ_(l) ^(insen)=0.2788

σ_(k) ^(avg)=0.8243, σ_(l) ^(avg)=0.8663, ρ_(k,l) ^(avg)=0.2969

P(C _(i) ^(sensitive))=0.2661, P(C _(i) ^(insensitive))=0.7339

[1257] Rule 32

[1258] Gene 1: *EST H49897 SID 429460 ESTs [5′:3′:AA007629]

[1259] Gene 2: TXNRD1 Thioredoxin reductase Chr.12 [510377 (IW)5′:AA055407 3′:AA055408]

[1260] Drug: Anthrapyrazole-derivative

[1261] Parameters:

μ_(k) ^(sen)=−0.8238, μ_(l) ^(sen)=0.8618

μ_(k) ^(insen)=0.2071, μ_(l) ^(insen)=−0.2166

σ_(k) ^(avg)=0.934, σ_(l) ^(avg)=0.9084, ρ_(k,l) ^(avg)=0.2681

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1262] Rule 33

[1263] Gene 1: PTN Pleiotrophin (heparin binding growth factor 8 neuritegrowth-promoting factor 1) Chr.7 [488801 (IW) 5′:AA045053 3′:AA045054]

[1264] Gene 2: TXNRD1 Thioredoxin reductase Chr.12 [510377 (IW)5′:AA055407 3′:AA055408]

[1265] Drug: Anthrapyrazole-derivative

[1266] Parameters:

μ_(k) ^(sen)=0.8776, μ_(l) ^(sen)=0.8618

μ_(k) ^(insen)=−0.2227, μ_(l) ^(insen)=−0.2166

σ_(k) ^(avg)=0.8932, σ_(l) ^(avg)=0.9084, ρ_(k,l) ^(avg)=−0.3478

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1267] Rule 34

[1268] Gene 1: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[1269] Gene 2: ESTs Chr.5 [322749 (I) 5′:3′:W15473]

[1270] Drug: Daunorubicin

[1271] Parameters:

μ_(k) ^(sen)=0.918, μ_(l) ^(sen)=−0.7006

μ_(k) ^(insen)=−0.2022, μ_(l) ^(insen)=0.1549

σ_(k) ^(avg)=0.8758, σ_(l) ^(avg)=0.9296, ρ_(k,l) ^(avg)=0.2797

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1272] Rule 35

[1273] Gene 1: L-LACTATE DEHYDROGENASE M CHAIN Chr. 11 [510595 (IW)5′:AA057759 3′:AA057760]

[1274] Gene 2: Homo sapiens T245 protein (T245) mRNA complete cds Chr.X[343063 (IW) 5′:W67989 3′:W68001]

[1275] Drug: Daunorubicin

[1276] Parameters:

μ_(k) ^(sen)=−0.7199, μ_(l) ^(sen)=−1.061

μ_(k) ^(insen)=0.1588, μ_(l) ^(insen)=−0.234

σ_(k) ^(avg)=0.9279, σ_(l) ^(avg)=0.8647, ρ_(k,l) ^(avg)=−0.2833

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1277] Rule 36

[1278] Gene 1: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[1279] Gene 2: SID W 510534 MAJOR GASTROINTESTINAL TUMOR-ASSOCIATEDPROTEIN GA733-2 PRECURSOR [5′:AA055858 3′:AA055808]

[1280] Drug: Daunorubicin

[1281] Parameters:

μ_(k) ^(sen)=0.918, μ_(l) ^(sen)=−0.437

μ_(k) ^(insen)=−0.2022, μ_(l) ^(insen)=0.09623

σ_(k) ^(avg)=0.8758, σ_(l) ^(avg)=0.9836, ρ_(k,l) ^(avg)=0.525

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1282] Rule 37

[1283] Gene 1: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1284] Gene 2: ESTsSID 429074 [5′:AA005275 3′:AA005169]

[1285] Drug: Daunorubicin

[1286] Parameters:

μ_(k) ^(sen)=−1.052, μ_(l) ^(sen)=−0.647

μ_(k) ^(insen)=0.2324, μ_(l) ^(insen)=0.1424

σ_(k) ^(avg)=0.8508, σ_(l) ^(avg)=0.9537, ρ_(k,l) ^(avg)=0.06225

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1287] Rule 38

[1288] Gene 1: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[1289] Gene 2: Human clone 23933 mRNA sequence Chr.17 [23933 (W)5′:T77288 3′:R39465]

[1290] Drug: Daunorubicin

[1291] Parameters:

μ_(k) ^(sen)=0.918, μ_(l) ^(sen)=0.4489

μ_(k) ^(insen)=−0.2022, μ_(l) ^(insen)=−0.09989

σ_(k) ^(avg)=0.8758, σ_(l) ^(avg)=1.004, ρ_(k,l) ^(avg)=−0.5196

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1292] Rule 39

[1293] Gene 1: GRL Glucocorticoid receptor Chr.5 [262691 (E)5′:3′:H99414]

[1294] Gene 2: *Prothymosin alpha SID W 271976 AMINOACYLASE-1 [5′:N446873′:N35315]

[1295] Drug: Daunorubicin

[1296] Parameters:

μ_(k) ^(sen)=0.3732, μ_(l) ^(sen)=−1.032

μ_(k) ^(insen)=−0.08233, μ_(l) ^(insen)=0.2284

σ_(k) ^(avg)=0.9501, σ_(l) ^(avg)=0.858, ρ_(k,l) ^(avg)=0.3514

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1297] Rule 40

[1298] Gene 1: *Prothymosin alpha SID W 271976 AMINOACYLASE-1 [5′:N446873′:N35315]

[1299] Gene 2: PLAUR Plasminogen activator urokinase receptor Chr.19[325077 (DIW) 5′:W49705 3′:W49706]

[1300] Drug: Daunorubicin

[1301] Parameters:

μ_(k) ^(sen)=−1.032, μ_(l) ^(sen)=0.1522

μ_(k) ^(insen)=0.2284, μ_(l) ^(insen)=−0.03346

σ_(k) ^(avg)=0.858, σ_(l) ^(avg)=0.9987, ρ_(k,l) ^(avg)=0.5897

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1302] Rule 41

[1303] Gene 1: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1304] Gene 2: ESTs Chr.2 [365120 (IW) 5′:AA025204 3′:AA025124]

[1305] Drug: Daunorubicin

[1306] Parameters:

μ_(k) ^(sen)=−1.052, μ_(l) ^(sen)=0.2085

μ_(k) ^(insen)=0.2324, μ_(l) ^(insen)=−0.04633

σ_(k) ^(avg)=0.8508, σ_(l) ^(avg)=1.018, ρ_(k,l) ^(avg)=0.376

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1307] Rule 42

[1308] Gene 1: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1309] Gene 2: Ribosomal protein L17SID 60561 [5′:T39375 3′:T40540]

[1310] Drug: Daunorubicin

[1311] Parameters:

μ_(k) ^(sen)=−1.052, μ_(l) ^(sen)=−0.5213

μ_(k) ^(insen)=0.2324, μ_(l) ^(insen)=0.1147

σ_(k) ^(avg)=0.8508, σ_(l) ^(avg)=0.9713, ρ_(k,l) ^(avg)=−0.2356

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1312] Rule 43

[1313] Gene 1: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1314] Gene 2: Glutathione S-Tranferase M1a-log

[1315] Drug: Daunorubicin

[1316] Parameters:

μ_(k) ^(sen)=−1.052, μ_(l) ^(sen)=0.1809

μ_(k) ^(insen)=0.2324, μ_(l) ^(insen)=−0.03737

σ_(k) ^(avg)=0.8508, σ_(l) ^(avg)=1.033, ρ_(k,l) ^(avg)=0.1657

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1317] Rule 44

[1318] Gene 1: SID 260288 ESTs [5′:H97716 3′:H96798]

[1319] Gene 2: SID W 358185 Human mitochondrial 2,4-dienoyl-CoAreductase mRNA complete cds [5′:W95455 3′:W95406]

[1320] Drug: Daunorubicin

[1321] Parameters:

μ_(k) ^(sen)=−0.9929, μ_(l) ^(sen)=−0.5507

μ_(k) ^(insen)=0.2192, μ_(l) ^(insen)=0.1224

σ_(k) ^(avg)=0.9063, σ_(l) ^(avg)=0.9734, ρ_(k,l) ^(avg)=−0.4799

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1322] Rule 45

[1323] Gene 1: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1324] Gene 2: L-LACTATE DEHYDROGENASE M CHAIN Chr.11 [510595 (IW)5′:AA057759 3′:AA057760]

[1325] Drug: Daunorubicin

[1326] Parameters:

μ_(k) ^(sen)=−1.052, μ_(l) ^(sen)=−0.7199

μ_(k) ^(insen)=0.2324, μ_(l) ^(insen)=−0.1588

σ_(k) ^(avg)=0.8508, σ_(l) ^(avg)=0.9279, ρ_(k,l) ^(avg)=−0.1035

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1327] Rule 46

[1328] Gene 1: SID W 471763 Crystallin zeta (quinone reductase)[5′:AA035179 3′:AA035180]

[1329] Gene 2: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1330] Drug: Daunorubicin

[1331] Parameters:

μ_(k) ^(sen)=−0.5185, μ_(l) ^(sen)=−1.052

μ_(k) ^(insen)=0.1147, μ_(l) ^(insen)=0.2324

σ_(k) ^(avg)=0.9683, σ_(l) ^(avg)=0.8508, ρ_(k,l) ^(avg)=−0.06753

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1332] Rule 47

[1333] Gene 1: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[1334] Gene 2: SID W 489301 ESTs [5′:AA054471 3′:AA058511]

[1335] Drug: Daunorubicin

[1336] Parameters:

μ_(k) ^(sen)=0.918, μ_(l) ^(sen)=0.7391

μ_(k) ^(insen)=−0.2022, μ_(l) ^(insen)=−0.1637

σ_(k) ^(avg)=0.8758, σ_(l) ^(avg)=0.9515, ρ_(k,l) ^(avg)=−0.3077

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1337] Rule 48

[1338] Gene 1: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1339] Gene 2: *Aldehyde reductase 1 (low Km aldose reductase) SID W418212 ESTs [5′:W90268 3′:W90593]

[1340] Drug: Daunorubicin

[1341] Parameters:

μ_(k) ^(sen)=−1.052, μ_(l) ^(sen)=0.09908

μ_(k) ^(insen)=0.2324, μ_(l) ^(insen)=−0.02151

σ_(k) ^(avg)=0.8508, σ_(l) ^(avg)=1.014, ρ_(k,l) ^(avg)=0.4702

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1342] Rule 49

[1343] Gene 1: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1344] Gene 2: SID W 484773 PYRROLINE-5-CARBOXYLATE REDUCTASE[5′:AA037688 3′:AA037689]

[1345] Drug: Daunorubicin

[1346] Parameters:

μ_(k) ^(sen)=−1.052, μ_(l) ^(sen)=−0.7351

μ_(k) ^(insen)=0.2324, μ_(l) ^(insen)=0.1628

σ_(k) ^(avg)=0.8508, σ_(l) ^(avg)=0.9291, ρ_(k,l) ^(avg)=−0.1858

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1347] Rule 50

[1348] Gene 1: SID W 484773 PYRROLINE-5-CARBOXYLATE REDUCTASE[5′:AA037688 3′:AA037689]

[1349] Gene 2: *Prothymosin alpha SID W 271976 AMINOACYLASE-1 [5′:N446873′:N35315]

[1350] Drug: Daunorubicin

[1351] Parameters:

μ_(k) ^(sen)=−0.7351, μ_(l) ^(sen)=−1.032

μ_(k) ^(insen)=0.1628, μ_(l) ^(insen)=0.2284

σ_(k) ^(avg)=0.9291, σ_(l) ^(avg)=0.858, ρ_(k,l) ^(avg)=0.2602

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1352] Rule 51

[1353] Gene 1: ESTs Chr.16 [154654 (RW) 5′:R55184 3′:R55185]

[1354] Gene 2: ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr. 16[429540 (W) 5′:AA011453 3′:AA011397]

[1355] Drug: Daunorubicin

[1356] Parameters:

μ_(k) ^(sen)=0.8271, μ_(l) ^(sen)=−0.994

μ_(k) ^(insen)=−0.1829, μ_(l) ^(insen)=0.2199

σ_(k) ^(avg)=0.9198, σ_(l) ^(avg)=0.8654, ρ_(k,l) ^(avg)=0.223

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1357] Rule 52

[1358] Gene 1: SID 234072 EST Highly similar to RETROVIRUS-RELATED POLPOLYPROTEIN [Homo sapiens] [5′:3′:H69001]

[1359] Gene 2: ESTs Chr.2 [149542 (DW) 5′:H00283 3′:H00284]

[1360] Drug: Daunorubicin

[1361] Parameters:

μ_(k) ^(sen)=−0.5103, μ_(l) ^(sen)=−1.052

μ_(k) ^(insen)=0.1131, μ_(l) ^(insen)=−0.2324

σ_(k) ^(avg)=0.9797, σ_(l) ^(avg)=0.8508, ρ_(k,l) ^(avg)=−0.1946

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1362] Rule 53

[1363] Gene 1: ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr. 16[429540 (IW) 5′:AA011453 3′:AA011397]

[1364] Gene 2: ESTs Chr.2 [365120 (IW) 5′:AA025204 3′:AA025124]

[1365] Drug: Amsacrine

[1366] Parameters:

μ_(k) ^(sen)=−0.7939, μ_(l) ^(sen)=0.558

μ_(k) ^(insen)=0.2239, μ_(l) ^(insen)=−0.1576

σ_(k) ^(avg)=0.8691, σ_(l) ^(avg)=0.9701, ρ_(k,l) ^(avg)=0.4985

P(C _(i) ^(sensitive))=0.22, P(C _(i) ^(insensitive))=0.78

[1367] Rule 54

[1368] Gene 1: SID W 489301 ESTs [5′:AA054471 3′:AA058511]

[1369] Gene 2: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW)5′:H01598 3′:H01495]

[1370] Drug: Pyrazoloimidazole

[1371] Parameters:

μ_(k) ^(sen)=0.9637, μ_(l) ^(sen)=0.7678

μ_(k) ^(insen)=−0.2165, μ_(l) ^(insen)=−0.1717

σ_(k) ^(avg)=0.8641, σ_(l) ^(avg)=0.9249, ρ_(k,l) ^(avg)=−0.4318

P(C _(i) ^(sensitive))=0.1833, P(C _(i) ^(insensitive))=0.8167

[1372] Rule 55

[1373] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1374] Gene 2: SID W 487113 Msh (Drosophila) homeo box homolog 1(formerly homeo box 7) [5′:AA045226 3′:AA045325]

[1375] Drug: CPT,10-OH

[1376] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.8196

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1876

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.8784, ρ_(k,l) ^(avg)=0.3086

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1377] Rule 56

[1378] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1379] Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds[5′:W79188 3′:W74434]

[1380] Drug: CPT,10-OH

[1381] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=1.001

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.2285

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.8549, ρ_(k,l) ^(avg)=−0.09544

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1382] Rule 57

[1383] Gene 1: SID W 510189 Homo sapiens CAG-isl 7 mRNA complete cds[5′:AA053648 3′:AA053259]

[1384] Gene 2: SID W 510534 MAJOR GASTROINTESTINAL TUMOR-ASSOCIATEDPROTEIN GA733-2 PRECURSOR [5′:AA055858 3′:AA055808]

[1385] Drug: CPT,10-OH

[1386] Parameters:

μ_(k) ^(sen)=0.4935, μ_(l) ^(sen)=−0.6863

μ_(k) ^(insen)=−0.1128, μ_(l) ^(insen)=0.1559

σ_(k) ^(avg)=0.9732, σ_(l) ^(avg)=0.9458, ρ_(k,l) ^(avg)=0.6221

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1387] Rule 58

[1388] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1389] Gene 2: COL4A1 Collagen type IV alpha 1 Chr.13 [489467 (IEW)5′:AA054624 3′:AA054564]

[1390] Drug: CPT,10-OH

[1391] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.8311

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1889

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9008, ρ_(k,l) ^(avg)=0.04514

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1392] Rule 59

[1393] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1394] Gene 2: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85PROTEINS [Gallus gallus] [5′:AA059424 3′:AA057835]

[1395] Drug: CPT,10-OH

[1396] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.8282

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1885

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9162, ρ_(k,l) ^(avg)=−0.1186

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1397] Rule 60

[1398] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1399] Gene 2: SID W 324073 Human lysyl oxidase-like protein mRNAcomplete cds [5′:W46647 3′:W465643]

[1400] Drug: CPT,10-OH

[1401] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.7583

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1738

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9205, ρ_(k,l) ^(avg)=0.2083

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1402] Rule 61

[1403] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1404] Gene 2: SID W 376472 Homo sapiens clone 24429 mRNA sequence[5′:AA041443 3′:AA041360]

[1405] Drug: CPT,10-OH

[1406] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.7273

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1653

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.927, ρ_(k,l) ^(avg)=0.02373

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1407] Rule 62

[1408] Gene 1: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1409] Gene 2: Homo sapiens (clone 35.3) DRAL mRNA complete cds Chr.2[324636 (IW) 5′:W46933 3′:W46835]

[1410] Drug: CPT,10-OH

[1411] Parameters:

μ_(k) ^(sen)=0.8729, μ_(l) ^(sen)=0.7843

μ_(k) ^(insen)=0.1997, μ_(l) ^(insen)=−0.1778

σ_(k) ^(avg)=0.8949, σ_(l) ^(avg)=0.9125, ρ_(k,l) ^(avg)=−0.1147

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1412] Rule 63

[1413] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1414] Gene 2: SID W 487878 SPARC/osteonectin [5′:AA046533 3′:AA045463]

[1415] Drug: CPT,10-OH

[1416] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.8472

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1926

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.898, ρ_(k,l) ^(avg)=0.04153

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1417] Rule 64

[1418] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1419] Gene 2:

[1420] Drug: CPT,10-OH

[1421] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.6293

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1436

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9536, ρ_(k,l) ^(avg)=0.1463

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1422] Rule 65

[1423] Gene 1: ESTs Chr.X [254029 (IRW) 5′:N75199 3′:N22323]

[1424] Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds[5′:W79188 3′:W74434]

[1425] Drug: CPT,10-OH

[1426] Parameters:

μ_(k) ^(sen)=0.1804, μ_(l) ^(sen)=1.001

μ_(k) ^(insen)=−0.04026, μ_(l) ^(insen)=−0.2285

σ_(k) ^(avg)=1.01, σ_(l) ^(avg)=0.8549, ρ_(k,l) ^(avg)=−0.04875

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1427] Rule 66

[1428] Gene 1: SID W 364810 ESTs [5′:AA034430 3′:AA053921]

[1429] Gene 2: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1430] Drug: CPT,10-OH

[1431] Parameters:

μ_(k) ^(sen)=−0.6399, μ_(l) ^(sen)=−0.9086

μ_(k) ^(insen)=0.1449, μ_(l) ^(insen)=0.2078

σ_(k) ^(avg)=0.9312, σ_(l) ^(avg)=0.8915, ρ_(k,l) ^(avg)=−0.1262

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1432] Rule 67

[1433] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1434] Gene 2: SID 257009 ESTs [5′:N39759 3′:N26801]

[1435] Drug: CPT,10-OH

[1436] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.5127

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1168

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9602, ρ_(k,l) ^(avg)=0.1779

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1437] Rule 68

[1438] Gene 1: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85PROTEINS [Gallus gallus] [5′:AA059424 3′:AA057835]

[1439] Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds[5′:W79188 3′:W74434]

[1440] Drug: CPT,10-OH

[1441] Parameters:

μ_(k) ^(sen)=0.8282, μ_(l) ^(sen)=1.001

μ_(k) ^(insen)=−0.1885, μ_(l) ^(insen)=−0.2285

σ_(k) ^(avg)=0.9162, σ_(l) ^(avg)=0.8549, ρ_(k,l) ^(avg)=0.18

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1442] Rule 69

[1443] Gene 1: ASNS Asparagine synthetase Chr.7 [510206 (W) 5′:AA0532133′:AA053461]

[1444] Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds[5′:W79188 3′:W74434]

[1445] Drug: CPT,10-OH

[1446] Parameters:

μ_(k) ^(sen)=−0.7243, μ_(l) ^(sen)=1.001

μ_(k) ^(insen)=0.1648, μ_(l) ^(insen)=−0.2285

σ_(k) ^(avg)=0.9358, σ_(l) ^(avg)=0.8549, ρ_(k,l) ^(avg)=−0.06293

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1447] Rule 70

[1448] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1449] Gene 2: Human extracellular protein (S1-5) mRNA complete cdsChr.2 [485875 (EW) 5′:AA040442 3′:AA040443]

[1450] Drug: CPT,10-OH

[1451] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.7657

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1743

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9202, ρ_(k,l) ^(avg)=−0.1283

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1452] Rule 71

[1453] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1454] Gene 2: Homo sapiens lysyl hydroxylase isoform 2 (PLOD2) mRNAcomplete cds Chr.3 [310449 (IW) 5′:W30982 3′:N98463]

[1455] Drug: CPT,10-OH

[1456] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.6335

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1445

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9558, ρ_(k,l) ^(avg)=0.1739

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1457] Rule 72

[1458] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1459] Gene 2: SID W 486110 Profilin 2 [5′:AA043167 3′:AA040703]

[1460] Drug: CPT,10-OH

[1461] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.7038

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1605

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9573, ρ_(k,l) ^(avg)=0.08051

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1462] Rule 73

[1463] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1464] Gene 2: SID 42787 ESTs [5′:R59827 3′:R59717]

[1465] Drug: CPT,10-OH

[1466] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.5759

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1318

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.961, ρ_(k,l) ^(avg)=0.06258

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1467] Rule 74

[1468] Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19[310021 (I) 5′:3′:N99151]

[1469] Gene 2: SID 50243 ESTs [5′:H17681 3′:H17066]

[1470] Drug: CPT,10-OH

[1471] Parameters:

μ_(k) ^(sen)=−0.9086, μ_(l) ^(sen)=0.8677

μ_(k) ^(insen)=0.2078, μ_(l) ^(insen)=−0.1977

σ_(k) ^(avg)=0.8915, σ_(l) ^(avg)=0.9058, ρ_(k,l) ^(avg)=−0.1472

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1472] Rule 75

[1473] Gene 1: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds[5′:W79188 3′:W74434]

[1474] Gene 2: SID 359504 ESTs [5′:3′:AA010589]

[1475] Drug: CPT,10-OH

[1476] Parameters:

μ_(k) ^(sen)=1.001, μ_(l) ^(sen)=−0.336

μ_(k) ^(insen)=−0.2285, μ_(l) ^(insen)=0.07633

σ_(k) ^(avg)=0.8549, σ_(l) ^(avg)=0.9733, ρ_(k,l) ^(avg)=0.3387

P(C _(i) ^(sensitive))=0.1856, P(C _(i) ^(insensitive))=0.8144

[1477] Rule 76

[1478] Gene 1: SID 39144 ESTs Weakly similar to Rep-8 [H.sapiens][5′:R51769 3′:R51770]

[1479] Gene 2: SID W 358526 ESTs [5′:W96039 3′:W94821]

[1480] Drug: CPT,20-ester (S)

[1481] Parameters:

μ_(k) ^(sen)=−0.8367, μ_(l) ^(sen)=−0.771

μ_(k) ^(insen)=0.2555, μ_(l) ^(insen)=−0.2359

σ_(k) ^(avg)=0.879881, σ_(l) ^(avg)=0.9049, ρ_(k,l) ^(avg)=−0.2237

P(C _(i) ^(sensitive))=0.2344, P(C _(i) ^(insensitive))=0.7656

[1482] Rule 77

[1483] Gene 1: SID 39144 ESTs Weakly similar to Rep-8 [H.sapiens][5′:R51769 3′:R51770]

[1484] Gene 2: SID W 509633 ESTs Moderately similar to Kryn [M.musculus][5′:AA045560 3′:AA045561]

[1485] Drug: CPT,20-ester (S)

[1486] Parameters:

μ_(k) ^(sen)=−0.8367, μ_(l) ^(sen)=−0.8637

μ_(k) ^(insen)=0.255, μ_(l) ^(insen)=−0.2643

σ_(k) ^(avg)=0.8798, σ_(l) ^(avg)=0.8771, ρ_(k,l) ^(avg)=0.2147

P(C _(i) ^(sensitive))=0.2344, P(C _(i) ^(insensitive))=0.7656

[1487] Rule 78

[1488] Gene 1: SID 39144 ESTs Weakly similar to Rep-8 [H.sapiens][5′:R51769 3′:R51770]

[1489] Gene 2: *Hs.648 Cut (Drosophila)-like 1 (CCAAT displacementprotein) SID W 26677 ESTs [5′:R13994 3′:R39117]

[1490] Drug: CPT,20-ester (S)

[1491] Parameters:

μ_(k) ^(sen)=−0.8367, μ_(l) ^(sen)=−0.652

μ_(k) ^(insen)=0.2555, μ_(l) ^(insen)=0.1999

σ_(k) ^(avg)=0.8798, σ_(l) ^(avg)=0.9431, ρ_(k,l) ^(avg)=−0.3363

P(C _(i) ^(sensitive))=0.2344, P(C _(i) ^(sensitive))=0.7656

[1492] Rule 79

[1493] Gene 1: SID W 510189 Homo sapiens CAG-isl 7 mRNA complete cds[5′:AA053648 3′:AA053259]

[1494] Gene 2: SID W 346510 Homo sapiens hCPE-R mRNA for CPE-receptorcomplete cds [5′:W79089 3′:W74492]

[1495] Drug: CPT

[1496] Parameters:

μ_(k) ^(sen)=0.4583, μ_(l) ^(sen)=−0.4683

μ_(k) ^(insen)=−0.161, μ_(l) ^(insen)=0.1634

σ_(k) ^(avg)=0.9838, σ_(l) ^(avg)=0.9573, ρ_(k,l) ^(avg)=0.6575

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(sensitive))=0.7406

[1497] Rule 80

[1498] Gene 1: ESTs Chr.19 [485804 (EW) 5′:AA040350 3′:AA040351]

[1499] Gene 2: Glyoxalase-I-log

[1500] Drug: CPT,20-ester (S)

[1501] Parameters:

μ_(k) ^(sen)=−0.7177, μ_(l) ^(sen)=−0.5058

μ_(k) ^(insen)=0.2573, μ_(l) ^(insen)=0.1814

σ_(k) ^(avg)=0.8936, σ_(l) ^(avg)=0.9632, ρ_(k,l) ^(avg)=−0.3337

P(C _(i) ^(sensitive))=0.2644, P(C _(i) ^(sensitive))=0.7356

[1502] Rule 81

[1503] Gene 1: Human G/T mismatch-specific thymine DNA glycosylase mRNAcomplete cds Chr.X [321997 (IW) 5′:W37234 3′:W37817]

[1504] Gene 2: SID W 358526 ESTs [5′:W96039 3′:W94821]

[1505] Drug: CPT,11-formyl (RS)

[1506] Parameters:

μ_(k) ^(sen)=0.626, μ_(l) ^(sen)=−1.055

μ_(k) ^(insen)=−0.151, μ_(l) ^(insen)=0.2536

σ_(k) ^(avg)=0.977, σ_(l) ^(avg)=0.8569, ρ_(k,l) ^(avg)=0.3776

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(sensitive))=0.8061

[1507] Rule 82

[1508] Gene 1: SID W 135118 GATA-binding protein 3 [5′:R31441 3′:R31442]

[1509] Gene 2: SID W 358526 ESTs [5′:W96039 3′:W94821]

[1510] Drug: CPT,11-formyl (RS)

[1511] Parameters:

μ_(k) ^(sen)=0.9817, μ_(l) ^(sen)=−1.055

μ_(k) ^(insen)=−0.2359, μ_(l) ^(insen)=0.2536

σ_(k) ^(avg)=0.9021, σ_(l) ^(avg)=0.8569, ρ_(k,l) ^(avg)=−0.08481

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(sensitive))=0.8061

[1512] Rule 83

[1513] Gene 1: ESTs Chr.16 [154654 (W) 5′:R55184 3′:R55185]

[1514] Gene 2: SOD2 Superoxide dismutase 2 mitochondrial Chr.6 [144758(EW) 5′:R76245 3′:R76527]

[1515] Drug: CPT,11-formyl (RS)

[1516] Parameters:

μ_(k) ^(sen)=0.874, μ_(l) ^(sen)=−0.7046

μ_(k) ^(insen)=−0.2102, μ_(l) ^(insen)=0.1693

σ_(k) ^(avg)=0.9112, σ_(l) ^(avg)=0.9543, ρ_(k,l) ^(avg)=0.3184

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(sensitive))=0.8061

[1517] Rule 84

[1518] Gene 1: SID W 358526 ESTs [5′:W96039 3′:W94821]

[1519] Gene 2: Glutathione S-Tranferase A1-log

[1520] Drug: CPT,11-formyl (RS)

[1521] Parameters:

μ_(k) ^(sen)=−1.055, μ_(l) ^(sen)=−0.6283

μ_(k) ^(insen)=0.2536, μ_(l) ^(insen)=0.1488

σ_(k) ^(avg)=0.8569, σ_(l) ^(avg)=0.9702, ρ_(k,l) ^(avg)=−0.125

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(sensitive))=0.8061

[1522] Rule 85

[1523] Gene 1: SID W 358526 ESTs [5′:W96039 3′:W94821]

[1524] Gene 2: PIGF Phosphatidylinositol glycan class F Chr.2 [486751(IEW) 5′:AA042803 3′:AA044616]

[1525] Drug: CPT,11-formyl (RS)

[1526] Parameters:

μ_(k) ^(sen)=−1.055, μ_(l) ^(sen)=−0.4069

μ_(k) ^(insen)=0.2569, μ_(l) ^(insen)=0.09808

σ_(k) ^(avg)=0.8569, σ_(l) ^(avg)=1.003, ρ_(k,l) ^(avg)=−0.3618

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(sensitive))=0.8061

[1527] Rule 86

[1528] Gene 1; PROTEASOME COMPONENT C13 PRECURSOR Chr.6 [344774(IW)5′:W74742 3′:W74705]

[1529] Gene 2: SID W 484681 Homo sapiens ES/130 mRNA complete cds[5′:AA037568 3′:AA037487]

[1530] Drug: Mechlorethamine

[1531] Parameters:

μ_(k) ^(sen)=0.6562, μ_(l) ^(sen)=−0.8883

μ_(k) ^(insen)=−0.1565, μ_(l) ^(insen)=0.2119

σ_(k) ^(avg)=0.9627, σ_(l) ^(avg)=0.9254, ρ_(k,l) ^(avg)=−0.5304

P(C _(i) ^(sensitive))=0.1928, P(C _(i) ^(sensitive))=0.8072

[1532] Rule 87

[1533] Gene 1: SID 43609 ESTs [5′:H06454 3′:H06184]

[1534] Gene 2: SID W 53251 Human Zn-15 related zinc finger protein (rlf)mRNA complete cds [5′:R15988 3′:R15987]

[1535] Drug: Mechlorethamine

[1536] Parameters:

μ_(k) ^(sen)=1.042, μ_(l) ^(sen)=−0.5622

μ_(k) ^(insen)=−0.2493, μ_(l) ^(insen)=0.1345

σ_(k) ^(avg)=0.8728, σ_(l) ^(avg)=0.9712, ρ_(k,l) ^(avg)=0.3407

P(C _(i) ^(sensitive))=0.1928, P(C _(i) ^(sensitive))=0.8072

[1537] Rule 88

[1538] Gene 1: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW)5′:W48793 3′:W49619]

[1539] Gene 2: Homo sapiens (clone 35.3) DRAL mRNA complete cds Chr.2[324636 (IW) 5′:W46933 3′:W46835]

[1540] Drug: Geldanamycin

[1541] Parameters:

μ_(k) ^(sen)=−0.8842, μ_(l) ^(sen)=0.09839

μ_(k) ^(insen)=0.225, μ_(l) ^(insen)=−0.02426

σ_(k) ^(avg)=0.8839, σ_(l) ^(avg)=1, ρ_(k,l) ^(avg)=−0.6697

P(C _(i) ^(sensitive))=0.2033, P(C _(i) ^(sensitive))=0.7967

[1542] Rule 89

[1543] Gene 1: ESTsSID 327435 [5′:W32467 3′:W19830]

[1544] Gene 2: ESTs Chr.3 [377430 (IW) 5′:AA055159 3′:AA055043]

[1545] Drug: Morpholino-adriamycin

[1546] Parameters:

μ_(k) ^(sen)=0.7559, μ_(l) ^(sen)=1.064

μ_(k) ^(insen)=−0.1508, μ_(l) ^(insen)=−0.212

σ_(k) ^(avg)=0.9646, σ_(l) ^(avg)=0.9006, ρ_(k,l) ^(avg)=−0.2502

P(C _(i) ^(sensitive))=0.1661, P(C _(i) ^(sensitive))=0.8339

[1547] Quadratic Discriminant Analysis—2-dimensional (QDA 2D)

[1548] This method computes a Bayesian conditional probability P(jεC_(i)^(sensitive)|g^(k) ^(j), g_(l) ^(j)) that a cell line j is sensitive todrug i, given the abundances of genes k and l, g_(k) ^(j) and g_(l)^(j), respectively, in cell line j.

[1549] The probability is computed using the following equation:${ {{{P( {j \in C_{i}^{sensitive}} }g_{k}^{j}},g_{l}^{j}} ) = \frac{\quad_{i}{G_{k,l}^{sensitive}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{sensitive} )}}{{\quad_{i}{G_{k,l}^{sensitive}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{sensitive} )}} +_{i}{{G_{k,l}^{insensitive}( {g_{k}^{j},g_{l}^{j}} )} \cdot {P( C_{i}^{insensitive} )}}}},$

[1550] where

P(C _(i) ^(sensitive))=prior probability of the sensitive set=|C _(i)^(sensitive)|/(C _(i) ^(sensitive) |+|C _(i) ^(sensitive)|),

P(C _(i) ^(insensitive))=prior probability of the insensitive set=|C_(i) ^(insensitive)|/(C _(i) ^(sensitive) |+|C _(i) ^(insensitive)|),

[1551]_(i)G_(k,l) ^(sensitive)(g_(k) ^(j), g_(l) ^(j))=joint probabilityof abundance values g_(k) ^(j) and g_(l) ^(j) from the bivariategaussian density fitted to the histogram of gene k and l abundances overthe sensitive cell lines when subjected to drug i.$\quad_{i}{G_{k,l}^{sensitive}( {g_{k}^{j},g_{l}^{j}} )} = {\frac{1}{2{\pi\sigma}_{k}^{sen}\sigma_{l}^{sen}\sqrt{1 - ( \rho_{k,l}^{sen} )^{2}}}\exp \{ \frac{- \lbrack {( \frac{g_{k}^{j} - \mu_{k}^{sen}}{\sigma_{k}^{sen}} )^{2} - {2{\rho_{k,l}^{sen}( \frac{g_{k}^{j} - \mu_{k}^{sen}}{\sigma_{k}^{sen}} )}( \frac{g_{l}^{j} - \mu_{l}^{sen}}{\sigma_{l}^{sen}} )} + ( \frac{g_{l}^{j} - \mu_{l}^{sen}}{\sigma_{l}^{sen}} )^{2}} \rbrack}{2( {1 - ( \rho_{k,l}^{sen} )^{2}} )} \}}$

[1552] where

[1553] μ_(k) ^(sen)=mean of gene k abundances over the sensitive celllines

[1554] σ_(k) ^(sen)=standard deviation of gene k abundances in thesensitive cell lines

[1555] μ_(l) ^(sen)=mean of gene 1 abundances over the sensitive celllines

[1556] σ_(l) ^(sen)=standard deviation of gene 1 abundances in thesensitive cell lines

[1557] ρ_(k,l) ^(sen)=correlation coefficient of gene k and gene labundances in the sensitive cell lines

[1558]_(i) ^(G) _(k,l) ^(insensitive)(g_(k) ^(j), g_(l) ^(j))=jointprobability of abundance values g_(k) ^(j) and g_(l) ^(j) from thebivariate gaussian density fitted to the histogram of gene k and labundances over the insensitive cell lines when subjected to drug i.$\quad_{i}{G_{k,l}^{insensitive}( {g_{k}^{j},g_{l}^{j}} )} = {\frac{1}{2{\pi\sigma}_{k}^{insen}\sigma_{l}^{insen}\sqrt{1 - ( \rho_{k,l}^{insen} )^{2}}}\exp \{ \frac{- \lbrack {( \frac{g_{k}^{j} - \mu_{k}^{insen}}{\sigma_{k}^{insen}} )^{2} - {2{\rho_{k,l}^{insen}( \frac{g_{k}^{j} - \mu_{k}^{insen}}{\sigma_{k}^{insen}} )}( \frac{g_{l}^{j} - \mu_{l}^{insen}}{\sigma_{l}^{insen}} )} + ( \frac{g_{l}^{j} - \mu_{l}^{insen}}{\sigma_{l}^{insen}} )^{2}} }{2( {1 - ( \rho_{k,l}^{insen} )^{2}} )} }$

[1559] where

[1560] μ_(k) ^(insen)=mean of gene k abundances over the insensitivecell lines

[1561] σ_(k) ^(insen)=standard deviation of gene k abundances in theinsensitive cell lines

[1562] μ_(l) ^(insen)=mean of gene 1 abundances over the insensitivecell lines

[1563] σ_(l) ^(insen)=standard deviation of gene 1 abundances in theinsensitive cell lines

[1564] ρ_(k,l) ^(insen)=correlation coefficient of gene k and gene labundances in the insensitive cell lines

[1565] Sample parameters for the QDA 2D analsis of the NCI60 datasetare:

[1566] Rule 1

[1567] Gene 1: BMI1 Murine leukemia viral (bmi-1) oncogene homologChr.10 [418004 (REW) 5′:W90704 3′:W90705]

[1568] Gene 2: Human small GTP binding protein Rab7 mRNA complete cdsChr.3 [486233 ([W) 5′:AA043679 3′:AA043680]

[1569] Drug: Baker's-soluble-antifoliate

[1570] Parameters:

μ_(k) ^(sen)=0.2314, μ_(l) ^(sen)=0.3177, σ_(k) ^(sen)=1.437, σ_(l)^(sen)=1.51, ρ_(k,l) ^(sen)=−0.06216

μ_(k) ^(insen)=−0.07175, μ_(l) ^(insen)=−0.0982, σ_(k) ^(insen)=0.7941,σ_(l) ^(insen)=0.7097, ρ_(k,l) ^(insen)=−0.3688

P(C _(i) ^(sensitive))=0.2361, P(C _(i) ^(insensitive))=0.7639

[1571] Rule 2

[1572] Gene 1: IL8 Interleukin 8 Chr.4 [328692 (DW) 5′:W40283 3′:W45324]

[1573] Gene 2: X-ray induction of CIP1/WAF1-log

[1574] Drug: Cyanomorpholinodoxorubicin

[1575] Parameters:

μ_(k) ^(sen)=0.856, μ_(l) ^(sen)=0.6131, σ_(k) ^(sen)=0.6623, σ_(l)^(sen)=0.9005, ρ_(k,l) ^(sen)=0.4391

μ_(k) ^(insen)=−0.224, μ_(l) ^(insen)=−0.1602, σ_(k) ^(insen)=0.9401,σ_(l) ^(insen)=0.9451, ρ_(k,l) ^(insen)=−0.5299

P(C _(i) ^(sensitive))=0.2067, P(C _(i) ^(insensitive))=0.7933

[1576] Rule 3

[1577] Gene 1: SID W 45954 H.sapiens mRNA for testican [5′:H086693′:H08670]

[1578] Gene 2: SID W 359443 Human ORF mRNA complete cds [5′:AA0107053′:AA010706]

[1579] Drug: Cyanomorpholinodoxorubicin

[1580] Parameters:

μ_(k) ^(sen)=0.8178, μ_(l) ^(sen)=0.7159, σ_(k) ^(sen)=0.9544, σ_(l)^(sen)=0.6062, ρ_(k,l) ^(sen)=−0.8806

μ_(k) ^(insen)=−0.2139, μ_(l) ^(insen)=−0.1865, σ_(k) ^(insen)=0.8419,σ_(l) ^(insen)=0.9949, ρ_(k,l) ^(insen)=−0.3109

P(C _(i) ^(sensitive))=0.2067, P(C _(i) ^(insensitive))=0.7933

[1581] Rule 4

[1582] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1583] Gene 2: ESTs Chr.1 [488132 (IW) 5′:AA047420 3′:AA047421]

[1584] Drug: Mitozolamide

[1585] Parameters:

μ_(k) ^(sen)=−1.008, μ_(l) ^(sen)=0.4755, σ_(k) ^(sen)=0.5668, σ_(l)^(sen)=0.3355, ρ_(k,l) ^(sen)=0.3703

μ_(k) ^(insen)=0.2536, μ_(l) ^(insen)=−0.1193, σ_(k) ^(insen)=0.9027,σ_(l) ^(insen)=0.1066, ρ_(k,l) ^(insen)=−0.2131

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1586] Rule 5

[1587] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1588] Gene 2: ZFP36 Zinc finger protein homologous to Zf-36 in mouseChr.19 [486668 (DIW) 5′:AA043477 3′:AA043478]

[1589] Drug: Mitozolamide

[1590] Parameters:

μ_(k) ^(sen)=−0.3906, μ_(l) ^(sen)=−1.008, σ_(k) ^(sen)=0.5337, σ_(l)^(sen)=0.5668, ρ_(k,l) ^(sen)=−0.1073

μ_(k) ^(insen)=0.09821, μ_(l) ^(insen)=0.2536, σ_(k) ^(insen)=1.044,σ_(l) ^(insen)=0.9027, ρ_(k,l) ^(insen)=−0.3729

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1591] Rule 6

[1592] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1593] Gene 2: SID W 323824 NADH-CYTOCHROME B5 REDUCTASE [5′:W462113′:W46212]

[1594] Drug: Mitozolamide

[1595] Parameters:

μ_(k) ^(sen)=−1.008, μ_(l) ^(sen)=0.2421, σ_(k) ^(sen)=0.5668, σ_(l)^(sen)=0.4385, ρ_(k,l) ^(sen)=−0.04634

μ_(k) ^(insen)=0.2536, μ_(l) ^(insen)=−0.06095, σ_(k) ^(insen)=0.9027,σ_(l) ^(insen)=1.078, ρ_(k,l) ^(insen)=−0.1944

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1596] Rule 7

[1597] Gene 1: ESTs Chr.6 [146640 (I) 5′:R80056 3′:R79962]

[1598] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY!!!! [H.sapiens] [5′:H94138 3′:H94064]

[1599] Drug: Mitozolamide

[1600] Parameters:

μ_(k) ^(sen)=−0.3763, μ_(l) ^(sen)=−1.008, σ_(k) ^(sen)=0.5482, σ_(l)^(sen)=0.5668, ρ_(k,l) ^(sen)=−0.7153

μ_(k) ^(insen)=0.09352, μ_(l) ^(insen)=0.2356, σ_(k) ^(insen)=1.034,σ_(l) ^(insen)=0.9027, ρ_(k,l) ^(insen)=−0.1007

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1601] Rule 8

[1602] Gene 1: SID 276915 ESTs [5′:N48564 3′:N39452]

[1603] Gene 2: SID 301144 ESTs [5′:W16630 3′:N78729]

[1604] Drug: Mitozolamide

[1605] Parameters:

μ_(k) ^(sen)=0.001165, μ_(l) ^(sen)=0.7785, σ_(k) ^(sen)=0.4, σ_(l)^(sen)=0.2994, ρ_(k,l) ^(sen)=−0.3594

μ_(k) ^(insen)=−0.0009506, μ_(l) ^(insen)=−0.1951, σ_(k) ^(insen)=1.068,σ_(l) ^(insen)=1.014, ρ_(k,l) ^(insen)=−0.2265

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1606] Rule 9

[1607] Gene 1: Homo sapiens HuUAP1 mRNA for UDP-N-acetylglucosaminepyrophosphorylase complete cds Chr.1 [486035 (DIW) 5′:AA0431093′:AA040861]

[1608] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1609] Drug: Mitozolamide

[1610] Parameters:

μ_(k) ^(sen)=0.3574, μ_(l) ^(sen)=1.008, σ_(k) ^(sen)=0.5869, σ_(l)^(sen)=0.5668, ρ_(k,l) ^(sen)=0.3711

μ_(k) ^(insen)=−0.09028, μ_(l) ^(insen)=0.2536, σ_(k) ^(insen)=1.028,σ_(l) ^(insen)=0.9027, ρ_(k,l) ^(insen)=−0.1971

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1611] Rule 10

[1612] Gene 1: SID W 510182 H.sapiens mRNA for kinase A anchor protein[5′:AA053156 3′:AA053135]

[1613] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1614] Drug: Mitozolamide

[1615] Parameters:

μ_(k) ^(sen)=−0.4282, μ_(l) ^(sen)=−1.008, σ_(k) ^(sen)=0.4124, σ_(l)^(sen)=0.5668, ρ_(k,l) ^(sen)=0.1487

μ_(k) ^(insen)=0.1064, μ_(l) ^(insen)=0.2536, σ_(k) ^(insen)=1.07, σ_(l)^(insen)=0.9027, ρ_(k,l) ^(insen)=0.03962

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1616] Rule 11

[1617] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1618] Gene 2: SID 488362 ESTs [5′:AA046764 3′:AA046492]

[1619] Drug: Mitozolamide

[1620] Parameters:

μ_(k) ^(sen)=−1.008, μ_(l) ^(sen)=0.5996, σ_(k) ^(sen)=0.5668, σ_(l)^(sen)=0.3048, ρ_(k,l) ^(sen)=−0.238

μ_(k) ^(insen)=0.2536, μ_(l) ^(insen)=−0.1504, σ_(k) ^(insen)=0.9027,σ_(l) ^(insen)=0.1035, ρ_(k,l) ^(insen)=0.1442

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1621] Rule 12

[1622] Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1623] Gene 2: ESTs Highly similar to HYPOTHETICAL 13.6 KD PROTEIN INNUP170-ILS1 INTERGENIC REGION [Saccharo Chr.12 [415646 (IW) 5′:W787223′:W80529]

[1624] Drug: Mitozolamide

[1625] Parameters:

μ_(k) ^(sen)=−1.008, μ_(l) ^(sen)=0.4566, σ_(k) ^(sen)=0.5668, σ_(l)^(sen)=0.413, ρ_(k,l) ^(sen)=0.02745

μ_(k) ^(insen)=0.2536, μ_(l) ^(insen)=−0.1139, σ_(k) ^(insen)=0.9027,σ_(l) ^(insen)=0.1038, ρ_(k,l) ^(insen)=0.3175

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1626] Rule 13

[1627] Gene 1: ESTs Weakly similar to R06B9.b [C.elegans] Chr.1 [365488(IW) 5′:AA009557 3′:AA009558]

[1628] Gene 2: SID W 380674 ESTs [5′:AA053720 3′:AA053711]

[1629] Drug: Mitozolamide

[1630] Parameters:

μ_(k) ^(sen)=0.5214, μ_(l) ^(sen)=1.093, σ_(k) ^(sen)=0.4503, σ_(l)^(sen)=1.032, ρ_(k,l) ^(sen)=0.2533

μ_(k) ^(insen)=−0.1312, μ_(l) ^(insen)=−0.2739, σ_(k) ^(insen)=1.016,σ_(l) ^(insen)=0.7614, ρ_(k,l) ^(insen)=−0.2896

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1631] Rule 14

[1632] Gene 1: ESTs Chr.1 [366242 (I) 5′:3′:AA025593]

[1633] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1634] Drug: Mitozolamide

[1635] Parameters:

μ_(k) ^(sen)=−0.2007, μ_(l) ^(sen)=−1.008, σ_(k) ^(sen)=0.4757, σ_(l)^(sen)=0.5668, ρ_(k,l) ^(sen)=−0.2512

μ_(k) ^(insen)=0.04952, μ_(l) ^(insen)=0.2536, σ_(k) ^(insen)=1.076,σ_(l) ^(insen)=0.9027, ρ_(k,l) ^(insen)=−0.1109

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1636] Rule 15

[1637] Gene 1: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[1638] Gene 2: SID 147338 ESTs [5′:3′:H01302]

[1639] Drug: Cyclodisone

[1640] Parameters:

μ_(k) ^(sen)=0.6598, μ_(l) ^(sen)=0.1958, σ_(k) ^(sen)=0.2562, σ_(l)^(sen)=0.3673, ρ_(k,l) ^(sen)=−0.6593

μ_(k) ^(insen)=−0.1341, μ_(l) ^(insen)=−0.04021, σ_(k) ^(insen)=1.038,σ_(l) ^(insen)=1.061, ρ_(k,l) ^(insen)=0.2816

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[1641] Rule 16

[1642] Gene 1: SID W 51940 BETA-2-MICROGLOBULIN PRECURSOR [5′:H242363′:H24237]

[1643] Gene 2: SID W 486110 Profilin 2 [5′:AA043167 3′:AA040703]

[1644] Drug: Cyclodisone

[1645] Parameters:

μ_(k) ^(sen)=0.6766, μ_(l) ^(sen)=0.615, σ_(k) ^(sen)=0.5551, σ_(l)^(sen)=0.4072, ρ_(k,l) ^(sen)=−0.9224

μ_(k) ^(insen)=−0.1373, μ_(l) ^(insen)=−0.1252, σ_(k) ^(insen)=0.996,σ_(l) ^(insen)=1.031, ρ_(k,l) ^(insen)=−0.313

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[1646] Rule 17

[1647] Gene 1: Human DNA sequence from clone 14O9 on chromosomeXp11.1-11.4. Contains a Inter-Alpha-Trypsin Inh Chr.X [485194 (I)5′:AA039416 3′:AA039316]

[1648] Gene 2: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[1649] Drug: Cyclodisone

[1650] Parameters:

μ_(k) ^(sen)=0.2487, μ_(l) ^(sen)=0.6598, σ_(k) ^(sen)=0.4569, σ_(l)^(sen)=0.2562, ρ_(k,l) ^(sen)=−0.4186

μ_(k) ^(insen)=−0.05158, μ_(l) ^(insen)=−0.1341, σ_(k) ^(insen)=1.039,σ_(l) ^(insen)=1.038, ρ_(k,l) ^(insen)=0.2219

P(C _(i) ^(sensitive))=0.1689, P(C _(i) ^(insensitive))=0.8311

[1651] Rule 18

[1652] Gene 1: SID 512164 Human clathrin assembly protein 50 (AP50) mRNAcomplete cds [5′:3′:AA057396]

[1653] Gene 2: SID W 345624 Human homeobox protein (PHOX1) mRNA 3′ end[5′:W76402 3′:W72050]

[1654] Drug: Clomesone

[1655] Parameters:

μ_(k) ^(sen)=0.8248, μ_(l) ^(sen)=−0.253, σ_(k) ^(sen)=0.7407, σ_(l)^(sen)=0.7545, ρ_(k,l) ^(sen)=0.793

μ_(k) ^(insen)=−0.1956, μ_(l) ^(insen)=0.006021, σ_(k) ^(insen)=0.9082,σ_(l) ^(insen)=1.037, ρ_(k,l) ^(insen)=0.7103

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1656] Rule 19

[1657] Gene 1: MSN Moesin Chr.X [486864 (W) 5′:AA043008 3′:AA042882]

[1658] Gene 2: Human mRNA for reticulocalbin complete cds Chr.11 [485209(IW) 5′:AA039292 3′:AA039334]

[1659] Drug: Clomesone

[1660] Parameters:

μ_(k) ^(sen)=0.6791, μ_(l) ^(sen)=0.4913, σ_(k) ^(sen)=0.4486, σ_(l)^(sen)=0.4435, ρ_(k,l) ^(sen)=0.8962

μ_(k) ^(insen)=−0.1612, μ_(l) ^(insen)=−0.1165, σ_(k) ^(insen)=1.026,σ_(l) ^(insen)=1.058, ρ_(k,l) ^(insen)=0.04721

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1661] Rule 20

[1662] Gene 1: SID W 36809 Homo sapiens neural cell adhesion molecule(CALL) mRNA complete cds [5′:R34648 3′:R49177]

[1663] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1664] Drug: Clomesone

[1665] Parameters:

μ_(k) ^(sen)=0.6335, μ_(l) ^(sen)=1.184, σ_(k) ^(sen)=0.7063, σ_(l)^(sen)=0.9042, ρ_(k,l) ^(sen)=0.2103

μ_(k) ^(insen)=−0.1498, μ_(l) ^(insen)=−0.2817, σ_(k) ^(insen)=0.9826,σ_(l) ^(insen)=0.7835, ρ_(k,l) ^(insen)=−0.3389

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1666] Rule 21

[1667] Gene 1: SID W 471748 ESTs [5′:AA035018 3′:AA035486]

[1668] Gene 2: SID 147338 ESTs [5′:3′:H01302]

[1669] Drug: Clomesone

[1670] Parameters:

μ_(k) ^(sen)=1.066, μ_(l) ^(sen)=0.1604, σ_(k) ^(sen)=0.9178, σ_(l)^(sen)=0.37, ρ_(k,l) ^(sen)=−0.3953

μ_(k) ^(insen)=−0.2526, μ_(l) ^(insen)=−0.03847, σ_(k) ^(insen)=0.7849,σ_(l) ^(insen)=1.074, ρ_(k,l) ^(insen)=0.494

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1671] Rule 22

[1672] Gene 1: ESTs Chr.X [48536 (E) 5′:H14669 3′:H14579]

[1673] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALUSUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5′:H94138 3′:H94064]

[1674] Drug: Clomesone

[1675] Parameters:

μ_(k) ^(sen)=0.8957, μ_(l) ^(sen)=1.079, σ_(k) ^(sen)=0.7433, σ_(l)^(sen)=0.7048, ρ_(k,l) ^(sen)=−0.6495

μ_(k) ^(insen)=0.2117, μ_(l) ^(insen)=0.2564, σ_(k) ^(insen)=0.8949,σ_(l) ^(insen)=0.8653, ρ_(k,l) ^(insen)=−0.08726

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1676] Rule 23

[1677] Gene 1: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1678] Gene 2: SID W 488333 ESTs [5′:AA046755 3′:AA046642]

[1679] Drug: Clomesone

[1680] Parameters:

μ_(k) ^(sen)=1.184, μ_(l) ^(sen)=−0.1604, σ_(k) ^(sen)=0.9042, σ_(l)^(sen)=0.8711, ρ_(k,l) ^(sen)=−0.1011

μ_(k) ^(insen)=−0.2817, μ_(l) ^(insen)=0.03825, σ_(k) ^(insen)=0.7835,σ_(l) ^(insen)=1.011, ρ_(k,l) ^(insen)=0.4544

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1681] Rule 24

[1682] Gene 1: ESTs Chr.8 [470141 (IW) 5′:AA029870 3′:AA029318]

[1683] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds[5′:AA043528 3′:AA043529]

[1684] Drug: Clomesone

[1685] Parameters:

μ_(k) ^(sen)=0.4978, μ_(l) ^(sen)=1.184, σ_(k) ^(sen)=0.4895, σ_(l)^(sen)=0.9042, ρ_(k,l) ^(sen)=0.6156

μ_(k) ^(insen)=−0.1176, μ_(l) ^(insen)=−0.2817, σ_(k) ^(insen)=1.056,σ_(l) ^(insen)=0.7835, ρ_(k,l) ^(insen)=0.1011

P(C _(i) ^(sensitive))=0.1917, P(C _(i) ^(insensitive))=0.8083

[1686] Rule 25

[1687] Gene 1: BINDING REGULATORY FACTOR Chr.1 [485933 (IW) 5′:AA0408193′:AA040156]

[1688] Gene 2: SID 43555 MALATE OXIDOREDUCTASE [5′:H13370 3′:H06037]

[1689] Drug: Fluorouracil (5FU)

[1690] Parameters:

μ_(k) ^(sen)=0.5584, μ_(l) ^(sen)=0.9686, σ_(k) ^(sen)=1.073, σ_(l)^(sen)=0.4053, ρ_(k,l) ^(sen)=−0.839

μ_(k) ^(insen)=−0.1082, μ_(l) ^(insen)=−0.1883, σ_(k) ^(insen)=0.9367,σ_(l) ^(insen)=0.9657, ρ_(k,l) ^(insen)=−0.3566

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[1691] Rule 26

[1692] Gene 1: ESTsSID 327435 [5′:W32467 3′:W19830]

[1693] Gene 2: SID 289361 ESTs [5′:N99589 3′:N92652]

[1694] Drug: Fluorouracil (5FU)

[1695] Parameters:

μ_(k) ^(sen)=0.9982, μ_(l) ^(sen)=0.03614, σ_(k) ^(sen)=1.157, σ_(l)^(sen)=0.186, ρ_(k,l) ^(sen)=−0.4795

μ_(k) ^(insen)=−0.1943, μ_(l) ^(insen)=−0.007432, σ_(k) ^(insen)=0.8258,σ_(l) ^(insen)=1.074, ρ_(k,l) ^(insen)=0.09915

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[1696] Rule 27

[1697] Gene 1: ESTsSID 327435 [5′:W32467 3′:W19830]

[1698] Gene 2: H.sapiens mRNA for Gal-beta(1-3/1-4)GlcNAcalpha-2,3-sialyltransferase Chr.11 [324181 (IW) 5′:W47425 3′:W47395]

[1699] Drug: Fluorouracil (5FU)

[1700] Parameters:

μ_(k) ^(sen)=0.9982, μ_(l) ^(sen)=−0.3532, σ_(k) ^(sen)=1.157, σ_(l)^(sen)=0.2383, ρ_(k,l) ^(sen)=0.01963

μ_(k) ^(insen)=−0.1943, μ_(l) ^(insen)=0.06805, σ_(k) ^(insen)=0.8258,σ_(l) ^(insen)=1.049, ρ_(k,l) ^(insen)=0.2537

P(C _(i) ^(sensitive))=0.1628, P(C _(i) ^(insensitive))=0.8372

[1701] Rule 28

[1702] Gene 1: SID W 116819 Homo sapiens clone 23887 mRNA sequence[5′:T93821 3′:T93776]

[1703] Gene 2: ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr.16[429540 (IW) 5′:AA011453 3′:AA011397]

[1704] Drug: Fluorodopan

[1705] Parameters:

μ_(k) ^(sen)=0.4215, μ_(l) ^(sen)=−0.3324, σ_(k) ^(sen)=1.115, σ_(l)^(sen)=1.519, ρ_(k,l) ^(sen)=0.5573

μ_(k) ^(insen)=−0.1101, μ_(l) ^(insen)=−0.0863, σ_(k) ^(insen)=0.9491,σ_(l) ^(insen)=0.7573, ρ_(k,l) ^(insen)=−0.786

P(C _(i) ^(sensitive))=0.2061, P(C _(i) ^(insensitive))=0.7939

[1706] Rule 29

[1707] Gene 1: ESTs Chr.14 [244047 (I) 5′:N45439 3′:N38807]

[1708] Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds[5′:3′:N92942]

[1709] Drug: Cyclocytidine

[1710] Parameters:

μ_(k) ^(sen)=0.536, μ_(l) ^(sen)=0.004825, σ_(k) ^(sen)=0.4307, σ_(l)^(sen)=0.232, ρ_(k,l) ^(sen)=0.1655

μ_(k) ^(insen)=−0.1816, μ_(l) ^(insen)=−0.002083, σ_(k) ^(insen)=1.03,σ_(l) ^(insen)=1.151, ρ_(k,l) ^(insen)=0.08986

P(C _(i) ^(sensitive))=0.2533, P(C _(i) ^(insensitive))=0.7467

[1711] Rule 30

[1712] Gene 1: SID W 510230 Homo sapiens (clone CC6) NADH-ubiquinoneoxidoreductase subunit mRNA 3′ end cds [5′:AA053568 3′:AA053557]

[1713] Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds[5′:3′:N92942]

[1714] Drug: Cyclocytidine

[1715] Parameters:

μ_(k) ^(sen)=0.1566, μ_(l) ^(sen)=0.04825, σ_(k) ^(sen)=0.4745, σ_(l)^(sen)=0.232, ρ_(k,l) ^(sen)=−0.4326

μ_(k) ^(insen)=−0.05336, μ_(l) ^(insen)=−0.002083, σ_(k) ^(insen)=1.116,σ_(l) ^(insen)=1.151, ρ_(k,l) ^(insen)=0.3113

P(C _(i) ^(sensitive))=0.2533, P(C _(i) ^(insensitive))=0.7467

[1716] Rule 31

[1717] Gene 1: DNA POLYMERASE EPSILON CATALYTIC SUBUNIT A Chr.12 [321207(IW) 5′:W52910 3′:AA037353]

[1718] Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds[5′:3′:N92942]

[1719] Drug: Cyclocytidine

[1720] Parameters:

μ_(k) ^(sen)=0.7918, μ_(l) ^(sen)=0.004825, σ_(k) ^(sen)=1.042, σ_(l)^(sen)=0.232, ρ_(k,l) ^(sen)=0.176

μ_(k) ^(insen)=−0.2694, μ_(l) ^(insen)=−0.002083, σ_(k) ^(insen)=0.762,σ_(l) ^(insen)=1.151, ρ_(k,l) ^(insen)=−0.06434

P(C _(i) ^(sensitive))=0.2533, P(C _(i) ^(insensitive))=0.7467

[1721] Rule 32

[1722] Gene 1: TXNRD1 Thioredoxin reductase Chr.12 [510377 (1W)5′:AA055407 3′:AA055408]

[1723] Gene 2: ESTs Chr.1 [362126 (I) 5′:AA001086 3′:AA001049]

[1724] Drug: Mitomycin

[1725] Parameters:

μ_(k) ^(sen)=0.9736, μ_(l) ^(sen)=0.4653, σ_(k) ^(sen)=0.752, σ_(l)^(sen)=0.3908, ρ_(k,l) ^(sen)=0.1693

μ_(k) ^(insen)=−0.2247, μ_(l) ^(insen)=−0.107, σ_(k) ^(insen)=0.8952,σ_(l) ^(insen)=1.053, ρ_(k,l) ^(insen)=0.3972

P(C _(i) ^(sensitive))=0.1872, P(C _(i) ^(insensitive))=0.8128

[1726] Rule 33

[1727] Gene 1: SID W 260223 Human mRNA for BST-1 complete cds [5′:N454173′:N32106]

[1728] Gene 2: TXNRD1 Thioredoxin reductase Chr.12 [510377 (IW)5′:AA055407 3′:AA055408]

[1729] Drug: Mitomycin

[1730] Parameters:

μ_(k) ^(sen)=0.1887, μ_(l) ^(sen)=0.9736, σ_(k) ^(sen)=0.6724, σ_(l)^(sen)=0.752, ρ_(k,l) ^(sen)=0.7526

μ_(k) ^(insen)=−0.04347, μ_(l) ^(insen)=−0.2247, σ_(k) ^(insen)=1.003,σ_(l) ^(insen)=0.8952, ρ_(k,l) ^(insen)=−0.007584

P(C _(i) ^(sensitive))=0.1872, P(C _(i) ^(insensitive))=0.8128

[1731] Rule 34

[1732] Gene 1: SCYA2 Small inducible cytokine A2 (monocyte chemotacticprotein 1 homologous to mouse Sig-je) Chr.17 [108837 (DIW) 5′:T778163′:T77817]

[1733] Gene 2: *Carbonic anhydrase II SID) 429288 [5′:AA0074563′:AA007360]

[1734] Drug: Anthrapyrazole-derivative

[1735] Parameters:

μ_(k) ^(sen)=0.8903, μ_(l) ^(sen)=−0.3723, σ_(k) ^(sen)=0.9679, σ_(l)^(sen)=0.694, ρ_(k,l) ^(sen)=−0.4114

μ_(k) ^(insen)=−0.224, μ_(l) ^(insen)=−0.09341, σ_(k) ^(insen)=0.8509,σ_(l) ^(insen)=1.03, ρ_(k,l) ^(insen)=0.4247

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1736] Rule 35

[1737] Gene 1: SID 356851 Homo sapiens mRNA for nucleolar protein hNop56[5′:3′:W86238]

[1738] Gene 2: Human extracellular protein (S1-5) mRNA complete cdsChr.2 [485875 (EW) 5′:AA040442 3′:AA040443]

[1739] Drug: Anthrapyrazole-derivative

[1740] Parameters:

μ_(k) ^(sen)=−0.216, μ_(l) ^(sen)=1.016, σ_(k) ^(sen)=0.6331, σ_(l)^(sen)=1.089, ρ_(k,l) ^(sen)=−0.6461

μ_(k) ^(insen)=0.05396, μ_(l) ^(insen)=−0.2548, σ_(k) ^(insen)=1, σ_(l)^(insen)=0.7749, ρ_(k,l) ^(insen)=0.2101

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1741] Rule 36

[1742] Gene 1: ALDH10 Aldehyde dehydrogenase 10 (fatty aldehydedehydrogenase) Chr.17 [208950 (EW) 5′:H63829 3′:H63779]

[1743] Gene 2: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[1744] Drug: Anthrapyrazole-derivative

[1745] Parameters:

μ_(k) ^(sen)=0.6212, μ_(l) ^(sen)=0.843, σ_(k) ^(sen)=0.6852, σ_(l)^(sen)=0.575, ρ_(k,l) ^(sen)=0.2169

μ_(k) ^(insen)=−0.1554, μ_(l) ^(insen)=−0.2115, σ_(k) ^(insen)=0.9606,σ_(l) ^(insen)=0.9263, ρ_(k,l) ^(insen)=−0.3119

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1746] Rule 37

[1747] Gene 1: Human extracellular protein (S1-5) mRNA complete cdsChr.2 [485875 (EW) 5′:AA040442 3′:AA040443]

[1748] Gene 2: SID W 415693 Homo sapiens mRNA for phosphatidylinositol4-kinase complete cds [5′:W78879 3′:W84724]

[1749] Drug: Anthrapyrazole-derivative

[1750] Parameters:

μ_(k) ^(sen)=1.016, μ_(l) ^(sen)=0.3712, σ_(k) ^(sen)=1.809, σ_(l)^(sen)=0.4463, ρ_(k,l) ^(sen)=−0.3426

μ_(k) ^(insen)=−0.2548, μ_(l) ^(insen)=−0.09229, σ_(k) ^(insen)=0.7749,σ_(l) ^(insen)=1.066, ρ_(k,l) ^(insen)=0.341

P(C _(i) ^(sensitive))=0.2006, P(C _(i) ^(insensitive))=0.7994

[1751] Rule 38

[1752] Gene 1: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANEGLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5′:W76432 3′:W72039]

[1753] Gene 2: Human mRNA for KIAA0143 gene partial cds Chr.8 [488462(IW) 5′:AA047508 3′:AA047451]

[1754] Drug: Daunorubicin

[1755] Parameters:

μ_(k) ^(sen)=0.918, μ_(l) ^(sen)=−0.6559, σ_(k) ^(sen)=0.3704, σ_(l)^(sen)=0.4622, ρ_(k,l) ^(sen)=−0.5746

μ_(k) ^(insen)=−0.2022, μ_(l) ^(insen)=−0.1457, σ_(k) ^(insen)=0.9271,σ_(l) ^(insen)=1.007, ρ_(k,l) ^(insen)=−0.009774

P(C _(i) ^(sensitive))=0.1811, P(C _(i) ^(insensitive))=0.8189

[1756] Rule 39

[1757] Gene 1: SID W 162077 ESTs [5′:H25689 3′:H26271]

[1758] Gene 2: SID W 197549 ESTs [5′:R87793 3′:R87731]

[1759] Drug: Deoxydoxorubicin

[1760] Parameters:

μ_(k) ^(sen)=−0.2102, μ_(l) ^(sen)=−0.1107, σ_(k) ^(sen)=0.3133, σ_(l)^(sen)=0.9712, ρ_(k,l) ^(sen)=−0.98

μ_(k) ^(insen)=0.3539, μ_(l) ^(insen)=−0.01824, σ_(k) ^(insen)=1.068,σ_(l) ^(insen)=1.008, ρ_(k,l) ^(insen)=0.1725

P(C _(i) ^(sensitive))=0.1428, P(C _(i) ^(insensitive))=0.8572

[1761] Rule 40

[1762] Gene 1: ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr. 16[429540 (IW) 5′:AA011453 3′:AA011397]

[1763] Gene 2: ESTs Chr.2 [365120 (IW) 5′:AA025204 3′:AA025124]

[1764] Drug: Amsacrine

[1765] Parameters:

μ_(k) ^(sen)=−0.7939, μ_(l) ^(sen)=0.558, σ_(k) ^(sen)=1.022, σ_(l)^(sen)=1.102, ρ_(k,l) ^(sen)=0.7045

μ_(k) ^(insen)=0.2239, μ_(l) ^(insen)=−0.1576, σ_(k) ^(insen)=0.791,σ_(l) ^(insen)=0.8965, ρ_(k,l) ^(insen)=0.4064

P(C _(i) ^(sensitive))=0.22, P(C _(i) ^(insensitive))=0.78

[1766] Rule 41

[1767] Gene 1: G6PD Glucose-6-phosphate dehydrogenase Chr.X [430251 (IW)5′:AA010317 3′:AA010382]

[1768] Gene 2: SID W 376708 ESTs [5′:AA046358 3′:AA046274]

[1769] Drug: CPT,20-ester (S)

[1770] Parameters:

μ_(k) ^(sen)=−0.09704, μ_(l) ^(sen)=0.6823, σ_(k) ^(sen)=0.4911, σ_(l)^(sen)=0.8524, ρ_(k,l) ^(sen)=0.7542

μ_(k) ^(insen)=0.02995, μ_(l) ^(insen)=−0.2092, σ_(k) ^(insen)=1.068,σ_(l) ^(insen)=0.9393, ρ_(k,l) ^(insen)=−0.5785

P(C _(i) ^(sensitive))=0.2344, P(C _(i) ^(insensitive))=0.7656

[1771] Rule 42

[1772] Gene 1: H.sapiens mRNA for ESM-1 protein Chr.5 [324122 (RW)5′:W46667 3′:W465773]

[1773] Gene 2: Human FEZ2 mRNA partial cds Chr.2 [488055 (W) 5′:AA0585513′:AA053303]

[1774] Drug: CPT

[1775] Parameters:

μ_(k) ^(sen)=−0.1032, μ_(l) ^(sen)=0.8185, σ_(k) ^(sen)=0.4146, σ_(l)^(sen)=0.8985, ρ_(k,l) ^(sen)=−0.6229

μ_(k) ^(insen)=0.03592, μ_(l) ^(insen)=−0.2863, σ_(k) ^(insen)=1.124,σ_(l) ^(insen)=0.8401, ρ_(k,l) ^(insen)=0.4189

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[1776] Rule 43

[1777] Gene 1: SID W 361023 ESTs [5′:AA013072 3′:AA012983]

[1778] Gene 2: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW)5′:H01598 3′:H01495]

[1779] Drug: CPT

[1780] Parameters:

μ_(k) ^(sen)=−0.6506, μ_(l) ^(sen)=0.5667, σ_(k) ^(sen)=0.6739, σ_(l)^(sen)=1.274, ρ_(k,l) ^(sen)=0.7093

μ_(k) ^(insen)=0.2279, μ_(l) ^(insen)=−0.1978, σ_(k) ^(insen)=0.9778,σ_(l) ^(insen)=0.7508, ρ_(k,l) ^(insen)=−0.1771

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[1781] Rule 44

[1782] Gene 1: SID W 358754 Human mRNA for cysteine protease completecds [5′:W94449 3′:W94332]

[1783] Gene 2: SID W 159512 Integrin alpha 6 [5′:H16046 3′:H15934]

[1784] Drug: CPT

[1785] Parameters:

μ_(k) ^(sen)=−0.1082, μ_(l) ^(sen)=0.7291, σ_(k) ^(sen)=0.7356, σ_(l)^(sen)=0.6557, ρ_(k,l) ^(sen)=−0.6645

μ_(k) ^(insen)=0.0372, μ_(l) ^(insen)=−0.2559, σ_(k) ^(insen)=1.038,σ_(l) ^(insen)=0.9638, ρ_(k,l) ^(insen)=0.4712

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[1786] Rule 45

[1787] Gene 1: SID 257009 ESTs [5′:N39759 3′:N26801]

[1788] Gene 2: SID W 488148 H.sapiens mRNA for 3′UTR of unknown protein[5′:AA057239 3′:AA058703]

[1789] Drug: CPT

[1790] Parameters:

μ_(k) ^(sen)=0.3448, μ_(l) ^(sen)=0.8224, σ_(k) ^(sen)=0.7661, σ_(l)^(sen)=0.5588, ρ_(k,l) ^(sen)=0.6149

μ_(k) ^(insen)=−0.1208, μ_(l) ^(insen)=−0.2881, σ_(k) ^(insen)=1.029,σ_(l) ^(insen)=0.9329, ρ_(k,l) ^(insen)=0.06046

P(C _(i) ^(sensitive))=0.2594, P(C _(i) ^(insensitive))=0.7406

[1791] Rule 46

[1792] Gene 1: SID 43609 ESTs [5′:H06454 3′:H06184]

[1793] Gene 2: SID W 361023 ESTs [5′:AA013072 3′:AA012983]

[1794] Drug: CPT,20-ester (S)

[1795] Parameters:

μ_(k) ^(sen)=0.4667, μ_(l) ^(sen)=0.6333, σ_(k) ^(sen)=1.301, σ_(l)^(sen)=0.554, ρ_(k,l) ^(sen)=0.5266

μ_(k) ^(insen)=−0.1602, μ_(l) ^(insen)=−0.2168, σ_(k) ^(insen)=0.7751,σ_(l) ^(insen)=0.9858, ρ_(k,l) ^(insen)=0.2268

P(C _(i) ^(sensitive))=0.255, P(C _(i) ^(insensitive))=0.745

[1796] Rule 47

[1797] Gene 1: Human G/T mismatch-specific thymine DNA glycosylase mRNAcomplete cds Chr.X [321997 (IW) 5′:W37234 3′:W37817]

[1798] Gene 2: SID W 358526 ESTs [5′:W96039 3′:W94821]

[1799] Drug: CPT,11-formyl (RS)

[1800] Parameters:

μ_(k) ^(sen)=0.626, μ_(l) ^(sen)=1.055, σ_(k) ^(sen)=1.401, σ_(l)^(sen)=1.241, ρ_(k,l) ^(sen)=−0.1072

μ_(k) ^(insen)=−0.151, μ_(l) ^(insen)=−0.2536, σ_(k) ^(insen)=0.9295,σ_(l) ^(insen)=0.7034, ρ_(k,l) ^(insen)=0.6208

P(C _(i) ^(sensitive))=0.1939, P(C _(i) ^(insensitive))=0.8061

[1801] Rule 48

[1802] Gene 1: PROTEASOME COMPONENT C13 PRECURSOR Chr.6 [344774 (IW)5′:W74742 3′:W74705]

[1803] Gene 2: SID W 484681 Homo sapiens ES/130 mRNA complete cds[5′:AA037568 3′:AA037487]

[1804] Drug: Mechlorethamine

[1805] Parameters:

μ_(k) ^(sen)=0.6562, μ_(l) ^(sen)=−0.8883, σ_(k) ^(sen)=0.7248, σ_(l)^(sen)=0.7952, ρ_(k,l) ^(sen)=−0.1383

μ_(k) ^(insen)=−0.1656, μ_(l) ^(insen)=−0.2119, σ_(k) ^(insen)=0.9825,σ_(l) ^(insen)=0.9257, ρ_(k,l) ^(insen)=0.6324

P(C _(i) ^(sensitive))=0.1928, P(C _(i) ^(insensitive))=0.8072

[1806] Rule 49

[1807] Gene 1: AK1 Adenylate kinase 1 Chr.9 [488381 (IW) 5′:AA0467833′:AA0466533]

[1808] Gene 2: Human vascular endothelial growth factor related proteinVRP mRNA complete cds Chr.4 [309535 (I) 5′:3′:N94399]

[1809] Drug: Mechlorethamine

[1810] Parameters:

μ_(k) ^(sen)=−0.4881, μ_(l) ^(sen)=0.243, σ_(k) ^(sen)=1.786, σ_(l)^(sen)=0.4893, ρ_(k,l) ^(sen)=0.8105

μ_(k) ^(insen)=0.1157, μ_(l) ^(insen)=−0.05762, σ_(k) ^(insen)=0.6286,σ_(l) ^(insen)=1.08, ρ_(k,l) ^(insen)=0.03238

P(C _(i) ^(sensitive))=0.1928, P(C _(i) ^(insensitive))=0.8072

[1811] Rule 50

[1812] Gene 1: SID W489301 ESTs [5′:AA054471 3′:AA058511]

[1813] Gene 2: Human epithelial membrane protein (CL-20) mRNA completecds Chr.12 [488719 (IW) 5′:AA046077 3′:AA046025]

[1814] Drug: Melphalan

[1815] Parameters:

μ_(k) ^(sen)=0.9792, μ_(l) ^(sen)=−0.619, σ_(k) ^(sen)=1.075, σ_(l)^(sen)=0.7439, ρ_(k,l) ^(sen)=−0.8227

μ_(k) ^(insen)=−0.2399, μ_(l) ^(insen)=0.1515, σ_(k) ^(insen)=0.7994,σ_(l) ^(insen)=0.9531, ρ_(k,l) ^(insen)=0.3178

P(C _(i) ^(sensitive))=0.1967, P(C _(i) ^(insensitive))=0.8033

[1816] Rule 51

[1817] Gene 1: SID W 245450 Human transcription factor NFATx mRNAcomplete cds [5′:N77274 3′:N55066]

[1818] Gene 2: SID W 485645 KERATIN TYPE II CYTOSKELETAL 7 [5′:AA0398173′:AA041344]

[1819] Drug: 5-Hydroxypicolinaldehyde-thiose

[1820] Parameters:

μ_(k) ^(sen)=0.122, μ_(l) ^(sen)=0.8712, σ_(k) ^(sen)=0.2463, σ_(l)^(sen)=0.6735, ρ_(k,l) ^(sen)=0.1308

μ_(k) ^(insen)=−0.02658, μ_(l) ^(insen)=−0.1896, σ_(k) ^(insen)=1.091,σ_(l) ^(insen)=0.9271, ρ_(k,l) ^(insen)=0.05545

P(C _(i) ^(sensitive))=0.1789, P(C _(i) ^(insensitive))=0.8211

[1821] Rule 52

[1822] Gene 1: SID 381780 ESTs [5′:AA059257 3′:AA059223]

[1823] Gene 2: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85PROTEINS [Gallus gallus] [5′:AA059424 3′:AA057835]

[1824] Drug: Paclitaxel—Taxol

[1825] Parameters:

μ_(k) ^(sen)=0.1618, μ_(l) ^(sen)=0.8354, σ_(k) ^(sen)=0.1828, σ_(l)^(sen)=0.4935, ρ_(k,l) ^(sen)=−0.09957

μ_(k) ^(insen)=−0.03218, μ_(l) ^(insen)=0.162, σ_(k) ^(insen)=1.06,σ_(l) ^(insen)=0.9902, ρ_(k,l) ^(insen)=−0.09191

P(C _(i) ^(sensitive))=0.1622, P(C _(i) ^(insensitive))=0.8378

[1826] Rule 53

[1827] Gene 1: SID 381780 ESTs [5′:AA059257 3′:AA059223]

[1828] Gene 2: SID 130482 ESTs [5′:R21876 3′:R21877]

[1829] Drug: Paclitaxel—Taxol

[1830] Parameters:

μ_(k) ^(sen)=0.1618, μ_(l) ^(sen)=−0.9271, σ_(k) ^(sen)=0.1828, σ_(l)^(sen)=0.3413, ρ_(k,l) ^(sen)=−0.3935

μ_(k) ^(insen)=−0.03218, μ_(l) ^(insen)=0.1791, σ_(k) ^(insen)=1.06,σ_(l) ^(insen)=0.9842, ρ_(k,l) ^(insen)=−0.2741

P(C _(i) ^(sensitive))=0.1622, P(C _(i) ^(insensitive))=0.8378

[1831] Rule 54

[1832] Gene 1: SID 344786 Human mRNA for KIAA0177 gene partial cds[5′:3′:W74713]

[1833] Gene 2: TXNRD1 Thioredoxin reductase Chr.12 [510377 (IW)5′:AA055407 3′:AA055408]

[1834] Drug: Bisantrene

[1835] Parameters:

μ_(k) ^(sen)=−0.3189, μ_(l) ^(sen)=1.298, σ_(k) ^(sen)=0.6532, σ_(l)^(sen)=0.7515, ρ_(k,l) ^(sen)=0.9897

μ_(k) ^(insen)=−0.02732, μ_(l) ^(insen)=−0.1115, σ_(k) ^(insen)=0.9915,σ_(l) ^(insen)=0.9088, ρ_(k,l) ^(insen)=0.06623

P(C _(i) ^(sensitive))=0.07889, P(C _(i) ^(insensitive))=0.9211

[1836] Determining Statistical Significance of Finding

[1837] Mean Square Error (MSE) scores are calculated by comparing theprobabilities (a form of likelihood) computed by a method against anensemble of surrogate data generated by different randomizations, i.e.,permutations, of the original data (creating artificial samples). Aresulting histogram of MSE scores is then interpreted as representingthe probability distribution of error; hence, the statisticalsignificance of any given determined probability can be assigned. Thegene expression levels can then be selected according to the ranking oftheir probability for the original data, with a comparison against theMSE score for the randomized data.

[1838] Validating Predictions of Sensitivity to Drug, for Each Method

[1839] For any given gene k and drug I, a cross-validation procedure isused to assess validity of any prediction. For example, we omit 1 givencell line from consideration, and carry out a given method on theremaining cell lines, and record the findings. The omitted cell line isrestored and a different cell line is omitted, and the given methodre-applied. This is repeated, one cell line at a time, until all thecell lines have had their turn being omitted. All the findings arecompiled. Difference scores between an original calculation and a cellline-omitted calculation are obtained. Mean Square Errors (MSE) are thencalculated from the aggregated differences. MSE is then an assessment ofthe validity of the given method.

[1840] Sample results from one of the Bayesian classifiers (the LDA 2D)on the NCI60 dataset are shown in Table 8 below. TABLE 8 StatisticalSignificance - After Bonferroni Drug Gene 1 Gene 2 P-Value CorrectionAcivicin Glyoxalase-I-log Homo sapiens mRNA 5.947e−08 3.00% (RNAsynthesis for HYA22 complete inhibitor) cds Chr.3 [358957 (EW) 5′:W919693′:W94916] Baker's-soluble- SID W 254085 ESTs SID 118593 [5′:T928211.982e−08 1.00% antifoliate Moderately similar to 3′:T92741] (antifol)synaptonemal complex protein [M. musculus] [5′:N71532 3′:N22165]Baker's-soluble- SID W 254085 ESTs ESTs Chr.5 [46694 1.586e−07 7.90%antifoliate Moderately similar to (RW) 5′:H10240 (antifol) synaptonemalcomplex 3′:H10192] protein [M. musculus] [5′:N71532 3′:N22165]Mitozolamide SID W 242844 ESTs *Hs.648 Cut 5.947e−08 3.00% (alkylatingagent, Moderately similar to (Drosophila)-like 1 guanine-O6) !!!! ALUSUBFAMILY (CCAAT displacement J WARNING ENTRY protein) SID W 26677 !!!![H. sapiens] ESTs [5′:R13994 [5′:H94138 3′:H94064] 3′:R39117]Mitozolamide Homo sapiens delta7- SID W 380674 ESTs 1.388e−07 6.90%(alkylating agent, sterol reductase mRNA [5′:AA053720 guanine-O6)complete cds Chr.10 3′:AA053711] [417125 (E) 5′: 3′:W87472] MitozolamideGlutathoine S- *Hs.648 Cut 1.982e−07 9.90% (alkylating agent, TranferasePi-log (Drosophila)-like 1 guanine-O6) (CCAAT displacement protein) SIDW 26677 ESTs [5′:R13994 3′:R39117] Clomesone ESTs Chr.X [48536 (E) SID W242844 ESTs 1.982e−08 1.00% (alkylating agent, 5′:H14669 3′:H14579]Moderately similar to guanine-O6) !!!! ALU SUBFAMILY J WARNING ENTRY!!!! [H. sapiens] [5′:H94138 3′:H94064] Clomesone SID W 36809 Homo SID W487535 Human 1.982e−08 1.00% (alkylating agent, sapiens neural cell mRNAfor KIAA0080 guanine-O6) adhesion molecule gene partial cds (CALL) mRNA[5′:AA043528 complete cds 3′:AA043529] [5′:R34648 3′:R49177] ClomesoneM-PHASE INDUCER SID W 487535 Human 3.964e−08 2.00% (alkylating agent,PHOSPHATASE 2 mRNA for KIAA0080 guanine-O6) Chr.20 [179373 (EW) genepartial cds 5′:H50437 3′:H50438] [5′:AA043528 3′:AA043529] Clomesone SIDW 242844 ESTs SID 469842 Homo 3.964e−08 2.00% (alkylating agent,Moderately similar to sapiens mRNA for fatty guanine-O6) !!!! ALUSUBFAMILY acid binding protein J WARNING ENTRY complete cds !!!! [H.sapiens] [5′:AA0029794 [5′:H94138 3′:H94064] 3′:AA029795] ClomesoneESTsSID 327435 SID 469842 Homo 3.964e−08 2.00% (alkylating agent,[5′:W32467 3′:W19830] sapiens mRNA for fatty guanine-O6) acid bindingprotein complete cds [5′:AA029794 3′:AA029795] Clomesone SID 512164Human SID W 345624 Human 3.964e−08 2.00% (alkylating agent, clathrinassembly homeobox protein guanine-O6) protein 50 (AP50) (PHOX1) mRNA 3′end mRNA complete cds [5′:W76402 3′:W72050] [5′: 3′:AA057396] ClomesoneSID W 376951 ESTs SID W 487535 Human 3.964e−08 2.00% (alkylating agent,[5′:AA047756 mRNA for KIAA0080 guanine-O6) 3′:AA047641] gene partial cds[5′:AA043528 3′:AA043529] Clomesone Glutathoine S- SID W 487535 Human9.911e−08 5.00% (alkylating agent, Tranferase Pi-log mRNA for KIAA0080guanine-O6) gene partial cds [5′:AA043528 3′:AA043529] Clomesone XRCC4DNA repair SID W 242844 ESTs 9.911e−08 5.00% (alkylating agent, proteinXRCC4 Chr.5 Moderately similar to guanine-O6) [26811 (RW) !!!! ALUSUBFAMILY 5′:R14027 3′:R39148] J WARNING ENTRY !!!! [H. sapiens][5′:H94138 3′:H94064]

[1841] The above steps as performed on, by way of example, the NCI60dataset can be further explained as follows.

[1842] Start off with 2 tables of data: a table, T, with gene expressiondata and a table, A, with drug concentration data In table T each columnis a gene, each row is a cell line and each entry is the expressionlevel of a gene in a given cell line.

[1843] In table A, each column is a drug, each row is a cell line(corresponding exactly to the same cell lines in table T) and each entryis the drug concentration which inhibits the growth of a given cell lineby 50%.

[1844] Note: The same cell lines appear in Tables T and A, and the orderof the cell lines is the same in both tables. In the NCI60 analysisthere were 60 cell lines, 1000 genes and 90 drugs. TABLE T Gene 1 Gene 2Gene 3 Cell line 1 0.4 0.2 0.8 Cell line 2 0.5 0.4 0.3 Cell line 3 0.20.7 0.1

[1845] TABLE A Drug 1 Drug 2 Drug 3 Cell line 1 0.6 1.1 1.8 Cell line 20.1 0.4 0.3 Cell line 3 0.5 0.1 0.1

[1846] An example of Tables T and A with actual data are shown below:TABLE T Gene expression values Gene: Human GDP-dissociation Gene: SID Winhibitor protein 328550 ATL- Gene: RAC2 Ras- (Ly-GDI) mRNA derived PMA-related C3 complete cds responsive (APR) botulinum toxin Chr.12 [487374peptide substrate 2 Chr.22 (IW) [5′: W40533 [429908 (DI) 5′: 5′:AA0464823′: W40261] 3′:AA033975] 3′:AA046695] Cell line: −1.17 −0.93 −0.62CNS:SNB-19 Cell line: 0.19 0.1 −0.77 CNS:U251 Cell line: −1.2 −0.1 −0.45BR:BT-549

[1847] TABLE A −logGI50 values Drug: Thiopurine Drug: alpha-2′- Drug: (6MP) Deoxythioguanosine Thioguanine Cell line: −2.08 −2.35 −4.14CNS:SNB-19 Cell line: −0.77 −1.03 −1.63 CNS:U251 Cell line: −2.36 −1.6−0.47 BR:BT-549

[1848] 1) Transform the drug response values.

[1849] Form a new table which corresponds to the A table by transformingthe numerical values of Table A so that they fall on a continuousnumerical scale ≧0 and ≦1. This is done in order to represent theintensity of the attribute in a readily-interpretable manner: 0represents negligible insensity (e.g., insensitive to drug) and 1represents high intensity (e.g., sensitive to drug), with continuousgradation in between.

[1850] For example, using equation for the continuous piece-wise linearbiological scoring function described previously:

[1851] Let a_(ij) represent the entry in the ith row and jth column oftable A.

[1852] Transform each entry, a_(ij), as follows:

[1853] if a_(ij) is less than 0.3 then set a_(ij)=0

[1854] if a_(ij) is between 0.3 and 0.7, then seta_(ij)=(a_(ij)−0.7)/0.3

[1855] if a_(ij) is greater than or equal to 0.7, then set a_(ij)=1

[1856] If a new entry a_(ij) is >0, consider cell line i to be at leastpartially sensitive to drug j. If a new entry a_(ij) is less <1,consider cell line i to be at least partially insensitive to drug j.Based on the transformed attribute values in some column j, it ispossible to separate cell lines into 2 classes, C^(sensitive) andC^(insensitive). Cell lines that are sensitive are in classC^(sensitive) and cell lines that are insensitive are in theC^(insensitive) class. But, some cell lines can be considered to bepartially in both class. For example, if the transformed value a_(ij)=x,then cell line i is considered to be x*100% in class C^(sensitive) and(1−x)*100% in class C^(insensitive).

[1857]2) Example Application of Bayesian Classifiers—UGDA 1D, UGDA 2D,LDA 1D, QDA 1D, LDA 2D, QDA 2D.

[1858] Note: Steps explained using LDA 1D are equivalently applied forany of the other Bayesian classifiers.

EXAMPLE OF STEPS

[1859] Apply LDA 1D to measure how well a given gene co-occurs,associates with, or predicts response to a given drug.

[1860] 2.1) Select a column, T_(k), from the T matrix, with theexpression values of some gene k. Select a column, A_(i), from the Amatrix, with the drug concentrations (e.g., in units of −log₁₀GI50)values of some drug i [see paragraph 1d in the Methods document forGI50].

[1861] 2.2) Remove the first entry, T_(1,k), from column T_(k) and thefirst entry, A_(1,i), from column A_(i). Assume that these entriesbelong to cell line L₁.

[1862] 2.3) Separate the remaining entries, (T_(2,k) through T_(n,k)) incolumn T_(k) into two sets:

[1863] one set, _(i)C^(sensitive) has the gene expression values of celllines at least partially sensitive to drug i (i.e. these cell lines havevalues greater than 0 in column A_(i))

[1864] a second set, _(i)C^(insensitive), has the gene expression valuesof cell lines at least partially insensitive to drug i (i.e. these celllines have values smaller than 1 in column A_(i))

[1865] 2.4) Compute the weighted mean, μ_(k) ^(sensitive), and theweighted standard deviation, σ_(k) ^(sensitive), of the values in set_(i)C^(sensitive).

[1866] Find the weighted mean, μ_(k) ^(insensitive), and the weightedstandard deviation, σ_(k) ^(insensitive) of the values in set_(i)C^(insensitive).

[1867] Find the weighted average standard deviation σ_(k) ^(avg) of thetwo sets.

[1868] Find the frequency, P(_(i)C^(sensitive)), of the sensitive classand the frequency, P(_(i)C^(insensitive)), of the insensitive class.

[1869] Compute parameters necessary to fit any chosen mathematicaldensity function or continuous curve to a α category-wise histogram ofthe type described previously.

[1870] 2.5) Compute the probability, P(L₁εC^(sensitive)|T_(1,k)), thatcell line L₁ is sensitive to drug i, using the information of theexpression level of gene k and the proportion, i.e., frequency, of thesensitive and insensitive classes. Namely, compute${ {{P( {L_{1} \in C_{i}^{sensitive}} }T_{1,k}} ) = \frac{\quad_{i}{G_{k}^{sensitive}( T_{1,k} )} \cdot {P( C_{i}^{sensitive} )}}{{\quad_{i}{G_{k}^{sensitive}( T_{1,k} )} \cdot {P( C_{i}^{sensitive} )}} +_{i}{{G_{k}^{insensitive}( T_{1,k} )} \cdot {P( C_{i}^{insensitive} )}}}},\begin{matrix}{where} \\{\quad_{i}{G_{k}^{sensitive}( T_{1,k} )} = {\frac{1}{\sigma_{k}^{avg}\sqrt{2\pi}}e^{{{- {({T_{1,k} - \mu_{k}^{sensitive}})}^{2}}/2}{(\sigma_{k}^{avg})}^{2}}}} \\{\quad_{i}{G_{k}^{insensitive}( T_{1,k} )} = {\frac{1}{\sigma_{k}^{avg}\sqrt{2\pi}}e^{{{- {({T_{1,k} - \mu_{k}^{insensitive}})}^{2}}/2}{(\sigma_{k}^{avg})}^{2}}}}\end{matrix}$

[1871] as described previously.

[1872] 2.6) Calculate an error for the probability derived in step 2.5.

[1873] Consider the probability from step 2.5 to be the expectedprobability, p^(expected), that cell line L₁ is sensitive to drug i.Consider entry A_(1,i) to be the observed probability, p^(observed),that cell line L₁ is sensitive to drug i.

[1874] Then, calculate an error, E₁, based on these two values, whereE₁=(P^(expected)−P^(observed))².

[1875] 2.7) A cross-validation procedure.

[1876] For each cell line, find the probability of sensitivity to drugi.

[1877] Restore the first entries of columns T_(k) and A_(i), (entriesbelonging to cell line L₁) and remove the second entry of these columns.Assume that the removed entries belong to cell line C₂. Repeat steps 2.3through 2.6, to obtain the probability of cell line L₂ being sensitiveto drug i. Follow the same procedure for each of the cell lines. Findthe mean of the error terms, E, from all the iterations. This value isreferred to as the mean squared error (MSE). This MSE quantifies howwell gene k predicts sensitivity to drug i.

[1878] 3) Find the MSE scores of all genes versus all drugs.

[1879] 4) A statistical significance assessment procedure.

[1880] Find initial significance p-values for all MSE scores.

[1881] A significance p-value indicates the likelihood that an MSE scorecould have arisen by chance (i.e. that randomized data (i.e., theoriginal data, randomly permuted to obliterate any patterns that mayhave been in the original data) could have generated the MSE score).

[1882] 4.1) Construct a distribution, i.e., histogram, of MSE scoresfrom the LDA 1D being applied to randomized data.

[1883] In each column of the T table, randomly rearrange the order ofthe entries. In each column of the A table, randomly rearrange the orderof the entries. Make copies of these two tables, and again randomlyrearrange the entries in all columns. Repeat this procedure until thereare 100 randomized versions of the 2 tables. Apply steps 2 and 3 to eachof the randomized pairs of tables. In other words, for each pair oftables, find the MSE scores of all genes versus all drugs. This resultsin a total of 100,000 MSE scores (1000 scores for a single pair oftables*100 pairs of tables). Such scores are referred to as MSE^(rand).MSE scores from non-randomized tables are referred to as MSE^(nonrand)

[1884] 4.2) Compare MSE scores from non-randomized data tables to MSEfrom randomized data tables.

[1885] For a given MSE score, M_(i), from non-randomized tables,determine the fraction of MSE^(rand) scores which are lower than M_(i).This fraction is the significance p-value for score M_(i). Using thisapproach, determine the significance p-values for all MSE_(nonrand)scores.

[1886] 5) Adjust the significance p-values associated with MSE^(nonrand)scores to correct for multiple tests significance test being employed.

[1887] The initial significance p-values associated with MSE^(nonrand)scores may not necessarily fairly reflect the true statisticalsignificance because there were multiple significance tests employed.Thus, multiply each significance p-value by 1000 to take into accountthat 1000 genes were tested against each drug. This kind of adjustmentof statistical significance to account for multiple significance testsbeing employed is known in the statistical literature as the Bonferronimethod.

[1888] 6) Report by cell line and drug, the genes and the probabilitiesderived in step 2.5

[1889] 6.1) Particularly identify in the report those cell lines anddrugs for which there are genes for which the probability derived instep 2.5 is high, say >0.85, and ranked by smallest-to-largestsignificance p-score.

[1890] The examples set out above provide general principles that may beextended to other fields of study, and are not intended to limit thescope of the invention. For example, drug sensitivity levels reflectingthe inhibiting of growth could be replaced by drug sensitivity thatreflects toxic reactions to drugs. This could be useful in findingmarkers that indicate circumstances where a given drug not only does nothelp, but may cause harm (be toxic to non-diseased cells). Diagnostickits can then be derived to search for those markers in given patients.

[1891] Similarly, examples of characterizing attributes could be SNPs orproteins (proteomics).

[1892] The Bayesian classifiers are not limited to 1 dimensional or 2dimensional classifiers, rather any dimension of classifier could beused as appropriate for the chosen characterizing attribute set. Thismay or may not turn up additional significant likelihoods ofco-occurrences depending on the relationships of the attributes in thedataset. It is recognized that a brute force approach of carrying outall steps for all combinations of characterizing attributes andattributes sets of interests can require a great deal of time andcomputational power, particularly with higher order combinations ofattributes. Pre-processing techniques, such as those mentionedpreviously, can be employed to reduce the number of candidatecharacterizing attribute sets, and thus the amount of time andcomputational power required.

[1893] Alternate methods could be used to create artificial samples inplace of the randomizations suggested herein. The randomizations usedherein proved to be a simple and effective manner of creating theartificial samples.

[1894] In the examples provided above, two likelihood thresholds havebeen used. First, a likelihood threshold based upon the artificalsamples. Second, a likelihood threshold based upon the assignedlikelihoods being above a certain percentile of all assigned likelihoodsfor the relevant attribute of interest.

[1895] The likelihood threshold can also be based on a selectedthreshold based on empirical knowledge, statistically derivation, orotherwise. In order to capture all characterizing sets of interest, eventhose that could possibly lack statistical validity, the likelihoodthreshold could simply be set at zero. Expanding on this, the likelihoodthreshold could be a selected numerical threshold, or the thresholdcould be varied, to determine the effect on the results. The likelihoodthreshold need not be based on artificial or random data in order toderive useful results from the methods.

[1896] As we have seen, the likelihood thresholds could be a singlethreshold, or a combination of likelihoods thresholds.

[1897] The methods described herein can be embodied in a computerprogram running on an appropriate computing platform as shown in FIG. 9.The combination of the computing platform and computer program resultsin a system for determining co-occurrences of characterizing attributesand attribute sets of interest. Again, the examples shown in the Figuresare not intended to be limiting to the breadth of the invention. As willbe evident to those skilled in the art, other configurations ofcomputing platforms and computer programs are possible. For example, thecomputing platform could take the form of computer network with thecomputer program distributed about the network, or accessed by terminalsremote from that part of the computing platform running the computerprogram. For example, the computer program may be running on a computerthat is connected to and accessible through the Internet.

[1898] An example flow diagram for the preferred embodiment of softwareembodying the first base method described above is shown in FIG. 9.Similarly, an example general block diagram for an embodiment of asystem for determining co-occurrences of characterizing attributes andattributes of interest is shown in FIG. 10. In this example, a computerprogram 1001 is stored on computer storage media 1003 (such as a harddisk from which the computer program is loaded into memory of thecomputer at the time the program is run) of a standalone computer 1005.The dataset is stored in a database 1007 accessible to the computer1005. The ranked characterizing attribute sets resulting from the basemethods may be reported and stored in a file on the hard disk 1003 forlater use, including as an output display for viewing on a computermonitor 1009 of the computer 1005. They may take an alternative form ofoutput display as a report 1011 generated on a printer 1013. Similarly,they maybe reported to a file, or other output display across a computernetwork 1015.

[1899] Flow diagrams for embodiments of a number of other base methodsare shown in FIGS. 11, 13 and 15. Corresponding block diagrams are shownin FIGS. 12, 14 and 16.

[1900] The methods, system and other aspects of the embodimentsdescribed herein, and the invention, can be used to identify markers fordiagnosis, such as might form part of diagnostic kits or procedures usedto determine a disease or syndrome type of a patient. Similarly, theymay be used to identify markers for prognosis of a disease or syndromeof a patient, such as might form part of diagnostic kits or proceduresused to determine a disease or syndrome type of a patient. Similarly,they may be used to identify markers to determine whether a therapy ortreatment is appropriate for a patient, or other biological attribute ofa human or other living system. This can be done by identifying andattribute set to be tested for in the patient or other living system bycarrying out one or more of the base methods previously described.Although the methods, system and other aspects of the embodiments havebeen described primarily with respect to the use of gene levelexpression sets as attribute sets, the embodiments and the invention mayalso be applied to tissue or serum protein concentration sets, or bloodor tissue molecular marker sets, or microscopic or macroscopic clinicalobservables, or combinations thereof.

[1901] It will be understood by those skilled in the art that thisdescription is made with reference to the preferred embodiment and thatit is possible to make other embodiments employing the principles of theinvention which fall within its spirit and scope as defined by thefollowing claims.

We claim:
 1. A method of identifying one or more characterizingattributes for an object that are likely to co-occur with one or moreattributes of interest for the object, the method comprising the stepsof: Selecting one or more attribute sets of one or more characterizingattributes of the object, Selecting an attribute set of one or moreattributes of interest for the object, Assigning a likelihood for eachcharacterized attribute set that the attribute set occurs for the objectwhen the attribute set of interest occurs for the object, eachlikelihood determined using one or more Bayesian computable classifierson a dataset of attributes for a plurality of actual samples of theobject, Comparing each assigned likelihood against one or morelikelihood thresholds, and Reporting the assigned likelihoods of thecharacterizing attribute set based on the likelihood thresholds.
 2. Themethod of claim 1 or 7, wherein a likelihood threshold for eachcharacterizing attribute set is determined using the same Bayesianclassifiers as the assigned likelihood on a dataset of attributes for aplurality of artificial samples of the object.
 3. The method of claim 1or 7, wherein a likelihood threshold for each characterizing attributeset is determined by computing those characterizing attribute sets withan assigned likelihood above a given percentile of all assignedlikelihoods for the relevant attribute set.
 4. The method of claim 2 or24, wherein the artificial samples are created by randomizing the actualgene expression levels for the characterizing attributes.
 5. The methodof claim 2 or 24, wherein the artificial samples are created bytransposing the actual gene expression levels for each characterizingattribute to another characterizing attribute.
 6. The method of claim 1,wherein the assigned likelihoods of the remaining characterizingattribute sets are also compared against a second likelihood thresholddetermined by computing those characterizing attribute sets with anassigned likelihood above a given percentile of all assigned likelihoodsfor the relevant attribute set of interest.
 7. A method of identifying acharacterizing attribute for an object that is likely to co-occur withan attribute of interest for the object, the method comprising the stepsof: Selecting one characterizing attribute set of one or more attributesfor the object, Selecting an attribute of interest for the object,Assigning a likelihood for the characterized attribute set that theattribute occurs for the object when the attribute of interest occursfor the object, the assigned likelihood determined using a Bayesiancomputable classifier on a dataset of attributes for a plurality ofactual samples of the object, Comparing the assigned likelihood againsta likelihood threshold, and Reporting the assigned likelihood of thecharacterizing attribute set based on the likelihood threshold.
 8. Themethod of claim 7 or 24, wherein the characterizing attributes are geneexpression levels and the attribute of interest is a drug sensitivitylevel.
 9. The method of claim 1, wherein each characterizing attributeis a gene expression level and the attribute of interest is a drugsensitivity level.
 10. The method of claim 1, wherein eachcharacterizing attribute is a gene expression level and the attribute ofinterest is drug dose (absolute concentration or dose relative to somestandard dose) along an increasing, or decreasing, scale.
 11. The methodof claim 1, wherein each characterizing attribute is a gene expressionlevel and the attribute of interest is the dose of drug which causeshalf-maximal cellular growth rate.
 12. The method of claim 1, whereineach characterizing attribute is a gene expression level and theattribute of interest is —logarithm₁₀(dose), where dose is the dosewhich yields half-maximal total cell mass accumulating under otherwisestandard conditions.
 13. The method of claim 9, the drug sensitivitylevel represents growth inhibiting in diseased cells.
 14. The method ofclaim 9, the drug sensitivity level represents a lack of growthinhibiting in diseased cells.
 15. The method of claim 9, the drugsensitivity level represents patient toxicity in healthy cells.
 16. Themethod of claim 9, wherein the attributes are represented in a datasettaken from the NCI60 dataset.
 17. The method of claim 7 or 24, whereinthe Bayesian classifier is selected from a group consisting of lineardiscriminant analysis, quadratic discriminant analysis, and auniform/gaussian analysis.
 18. The method of claim 1, wherein theBayesian classifiers are selected from a group consisting of lineardiscriminant analysis, quadratic discriminant analysis, and auniform/gaussian analysis.
 19. The method of claim 1, wherein twoBayesian classifiers are used selected from a group consisting of lineardiscriminant analysis, quadratic discriminant analysis, and auniform/gaussian analysis.
 20. The method of claim 1, wherein oneBayesian classifier is used selected from a group consisting of lineardiscriminant analysis, quadratic discriminant analysis, and auniform/gaussian analysis.
 21. The method of claim 1, wherein theBayesian classifiers are linear discriminant analysis, quadraticdiscriminant analysis, and a uniform/gaussian analysis.
 22. The methodof claim 1, wherein the characterizing attribute sets ranked followingcomparison of the likelihood and the likelihood threshold are reported.23. The method of claim 22, wherein the ranked characterizing attributessets are reported to one of a group consisting of a computer readablefile stored on computer readable media, a printed report, and a computernetwork.
 24. A method of identifying one or more characterizingattributes for an object that are likely to co-occur with one or moreattributes of interest for the object, the method comprising the stepsof: selecting one or more attribute sets of one or more characterizingattributes of the object, selecting an attribute set of one or moreattributes of interest for the object, assigning a likelihood for eachcharacterized attribute set that the attribute set occurs for the objectwhen the attribute set of interest occurs for the object, eachlikelihood determined using one or more Bayesian computable classifierson a dataset of attributes for a plurality of actual samples of theobject, determining a likelihood significance for each assignedlikelihood using artificial samples, and ranking the assignedlikelihoods of the characterizing attribute set using the likelihoodsignificance.
 25. The method of claim 24, wherein the assignedlikelihoods are ranked by assigned likelihood and subranked bylikelihood significance.
 26. The method of claim 24, further comprisingthe steps of: comparing the assigned likelihood against a likelihoodthreshold, and reporting the assigned likelihood of the characterizingattribute set based on the likelihood threshold and the ranking of theassigned likelihood.
 27. A method of identifying one or morecharacterizing attributes for an object that are likely to co-occur withone or more attributes of interest for the object using a dataset ofsamples of attributes for the object, the method comprising accessingone of the systems of claim
 28. 28. A system for identifying one or morecharacterizing attributes for an object that are likely to co-occur withone or more attributes of interest for the object using a dataset ofsamples of attributes for the object, the system comprising: a computingplatform, and a computer program on a computer readable medium for useon the computer platform in association with the dataset, the computerprogram comprising: instructions to identify a characterizing attributefor an object that is likely to co-occur with an attribute of interestfor the object, by carrying out the steps of the method of claim 1, 7 or24.
 29. A computer program on a computer readable medium for use on acomputer platform in association with a dataset, the computer programcomprising: instructions to identify a characterizing attribute for anobject that is likely to co-occur with an attribute of interest for theobject, by carrying out the steps of the method of claim 1, 7 or
 24. 30.A method of drug discovery comprising the steps: identifyingcharacterizing attribute sets for interaction by the drug, wherein thestep of identifying comprises carrying out the steps of the method ofclaim 1, 7 or 24 for drug sensitive attributes of interest, andperforming screens for drugs where growth in cells having desirablyranked characterizing attribute sets is drug sensitive.
 31. A method ofidentifying markers for diagnostic kits used to determine if a treatmentis appropriate for a patient, the method comprising the steps:identifying a gene expression level set to be tested for in the patientby carrying out the steps of the method of claim 1, 7 or
 24. 32. Amethod of identifying markers for diagnosis is of a living system, themethod comprising the steps: identifying an attribute set to be testedfor in the living system by carrying out the steps of the method ofclaim 1, 7 or
 24. 33. A method of identifying markers for prognosis of aliving system, the method comprising the steps: identifying an attributeset to be tested for in the living system by carrying out the steps ofthe method of claim 1, 7 or
 24. 34. A method of identifying markers fordetermining the appropriateness of a therapy or treatment of a livingsystem, the method comprising the steps: identifying an attribute set tobe tested for in the living system by carrying out the steps of themethod of claim 1, 7 or
 24. 35. The method of claim 32, wherein thediagnosis is with respect to a disease or syndrome type of a patient.36. The method of claim 33, wherein the prognosis is with respect to adisease or syndrome type of a patient.
 37. The method of claim 32, 33 or34, wherein the attributes of the attribute set comprise proteinconcentrations.
 38. The method of claim 37, wherein the proteinconcentrations comprise tissue protein concentrations.
 39. The method ofclaim 37, wherein the protein concentrations comprise serum proteinconcentrations.
 40. The method of claim 32, 33 or 34, wherein theattributes of the attribute set comprise molecular markers.
 41. Themethod of claim 40, wherein the molecular markers comprise bloodmolecular markers.
 42. The method of claim 40, wherein the molecularmarkers comprise tissue molecular markers.
 43. The method of claim 32,33 or 34, wherein the attributes of the attribute set comprise clinicalobservables.
 44. The method of claim 43, wherein the clinicalobservables comprise microscopic clinical observables.
 45. The method ofclaim 43, wherein the clinical observables comprise macroscopic clinicalobservables.
 46. The method of claim 32, wherein the markers are fordiagnostic kits used in the diagnosis.
 47. The method of claim 32,wherein the markers are for diagnostic procedures used in the diagnosis.48. The method of claim 33, wherein the markers are for prognostic kitsused in the prognosis.
 49. The method of claim 33, wherein the markersare for prognostic procedures used in the prognosis.